update ressult

2025-09-11 09:39:02 +00:00
parent 3cbf74149a
commit 0d6091fc0c
33 changed files with 1429 additions and 875 deletions
--- a/data/task/cni/Hallucination/anls_pipeline/human-labels.csv
+++ b/data/task/cni/Hallucination/anls_pipeline/human-labels.csv
@@ -0,0 +1,82 @@
+filename,label
+ITE55336627_PASSEPORT_Abasse_GUEYE__Completed_0.jpg,Low
+ITE55340318_ID_kat__1__Completed_0.jpg,Low
+ITE55340822_passeport_pascal_2028_Completed_0.jpg,High
+ITE55341271_C_I_face_Completed_0.jpg,High
+ITE55341677_2023_-_CNI_Recto-Verso_Hassina_0.jpg,High
+ITE55343297_20250729_172626_Completed_0.jpg,High
+ITE55343716_CNI_Completed_0.jpg,High
+ITE55343716_CNI_Completed_1.jpg,High
+ITE55346966_17537774579547958575370370624241_Completed_0.jpg,Low
+ITE55347865_Snapchat-715567440_Completed_0.jpg,Low
+ITE55347866_Snapchat-1551171803_Completed_0.jpg,Low
+ITE55347926_DHONDT_CNI_0.jpg,High
+ITE55348016_image_Completed_0.jpg,High
+ITE55348464_IMG-20250721-WA0000_Completed_0.jpg,High
+ITE55348878_CNI_RECTO_0.jpg,Low
+ITE55348904_cni_verso_0.jpg,High
+ITE55349793_2_em_CNI_CHASTAING__0.jpg,High
+ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS_0.jpg,High
+ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS_0.jpg,Low
+ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS_0.jpg,Low
+ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS_1.jpg,Low
+ITE55351156_Passeport_Completed_0.jpg,High
+ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed_0.jpg,High
+ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed_1.jpg,High
+ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS_0.jpg,Low
+ITE55352984_CNI_Completed_0.jpg,Low
+ITE55352984_CNI_Completed_1.jpg,Low
+ITE55354550_20250728_113359_Completed_0.jpg,High
+ITE55354891_image_Completed_0.jpg,High
+image (1)_0.jpg,High
+image (1)_1.jpg,High
+image (10)_0.jpg,Low
+image (10)_1.jpg,Low
+image (11)_0.jpg,Low
+image (11)_1.jpg,Low
+image (12)_0.jpg,High
+image (12)_1.jpg,High
+image (13)_0.jpg,High
+image (13)_1.jpg,High
+image (14)_0.jpg,High
+image (15)_0.jpg,High
+image (16)_0.jpg,High
+image (17)_0.jpg,High
+image (17)_1.jpg,High
+image (18)_0.jpg,High
+image (18)_1.jpg,High
+image (18)_2.jpg,High
+image (18)_3.jpg,Low
+image (19)_0.jpg,High
+image (19)_1.jpg,High
+image (2)_0.jpg,High
+image (21)_0.jpg,High
+image (22)_0.jpg,High
+image (22)_1.jpg,High
+image (22)_2.jpg,High
+image (22)_3.jpg,High
+image (23)_0.jpg,High
+image (24)_0.jpg,High
+image (25)_0.jpg,High
+image (26)_0.jpg,High
+image (27)_0.jpg,High
+image (28)_0.jpg,High
+image (28)_1.jpg,High
+image (29)_0.jpg,High
+image (3)_0.jpg,High
+image (30)_0.jpg,High
+image (31)_0.jpg,High
+image (4)_0.jpg,High
+image (5)_0.jpg,Low
+image (6)_0.jpg,High
+image (6)_1.jpg,High
+image (6)_2.jpg,High
+image (6)_3.jpg,Low
+image (6)_4.jpg,High
+image (6)_5.jpg,High
+image (6)_6.jpg,High
+image (7)_0.jpg,High
+image (7)_1.jpg,High
+image (8)_0.jpg,High
+image (9)_0.jpg,High
+image_0.jpg,High
--- a/data/task/cni/Hallucination/anls_pipeline/per_image_anls.csv
+++ b/data/task/cni/Hallucination/anls_pipeline/per_image_anls.csv
@@ -1,24 +1,4 @@
 image,anls,hallucination_score,num_fields
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.414286,0.585714,14
-ITE55340318_ID_kat__1__Completed,0.851677,0.148323,15
-ITE55340822_passeport_pascal_2028_Completed,0.857143,0.142857,14
-ITE55341271_C_I_face_Completed,0.888889,0.111111,18
-ITE55346966_17537774579547958575370370624241_Completed,0.400000,0.600000,5
-ITE55347865_Snapchat-715567440_Completed,0.803922,0.196078,17
-ITE55347866_Snapchat-1551171803_Completed,0.774928,0.225072,18
-ITE55347926_DHONDT_CNI,0.931624,0.068376,18
-ITE55348016_image_Completed,0.882353,0.117647,17
-ITE55348464_IMG-20250721-WA0000_Completed,0.583492,0.416508,15
-ITE55348878_CNI_RECTO,0.991900,0.008100,17
-ITE55349793_2_em_CNI_CHASTAING_,0.760000,0.240000,15
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.723529,0.276471,17
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.848366,0.151634,17
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,0.812707,0.187293,14
-ITE55351156_Passeport_Completed,0.761364,0.238636,16
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.428571,0.571429,14
-ITE55352984_CNI_Completed,0.793939,0.206061,15
-ITE55354550_20250728_113359_Completed,0.843712,0.156288,14
-ITE55354891_image_Completed,0.882353,0.117647,17
 image (1),0.855357,0.144643,14
 image (11),0.440000,0.560000,15
 image (12),0.834921,0.165079,14
@@ -46,3 +26,23 @@ image (6),0.853383,0.146617,14
 image (8),0.843367,0.156633,14
 image (9),0.661905,0.338095,15
 image,0.367946,0.632054,15
+ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.414286,0.585714,14
+ITE55340318_ID_kat__1__Completed,0.851677,0.148323,15
+ITE55340822_passeport_pascal_2028_Completed,0.857143,0.142857,14
+ITE55341271_C_I_face_Completed,0.888889,0.111111,18
+ITE55346966_17537774579547958575370370624241_Completed,0.400000,0.600000,5
+ITE55347865_Snapchat-715567440_Completed,0.803922,0.196078,17
+ITE55347866_Snapchat-1551171803_Completed,0.774928,0.225072,18
+ITE55347926_DHONDT_CNI,0.931624,0.068376,18
+ITE55348016_image_Completed,0.882353,0.117647,17
+ITE55348464_IMG-20250721-WA0000_Completed,0.583492,0.416508,15
+ITE55348878_CNI_RECTO,0.991900,0.008100,17
+ITE55349793_2_em_CNI_CHASTAING_,0.760000,0.240000,15
+ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.723529,0.276471,17
+ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.848366,0.151634,17
+ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,0.812707,0.187293,14
+ITE55351156_Passeport_Completed,0.761364,0.238636,16
+ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.428571,0.571429,14
+ITE55352984_CNI_Completed,0.793939,0.206061,15
+ITE55354550_20250728_113359_Completed,0.843712,0.156288,14
+ITE55354891_image_Completed,0.882353,0.117647,17
--- a/data/task/cni/IQA_results/brisque_per_image.csv
+++ b/data/task/cni/IQA_results/brisque_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,33.205000
-ITE55340318_ID_kat__1__Completed,52.446000
-ITE55340822_passeport_pascal_2028_Completed,67.004000
-ITE55341271_C_I_face_Completed,81.573000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,35.273000
-ITE55343297_20250729_172626_Completed,65.533000
-ITE55343716_CNI_Completed,101.605000
-ITE55346966_17537774579547958575370370624241_Completed,58.592000
-ITE55347865_Snapchat-715567440_Completed,60.966000
-ITE55347866_Snapchat-1551171803_Completed,62.116000
-ITE55347926_DHONDT_CNI,115.342000
-ITE55348016_image_Completed,47.489000
-ITE55348464_IMG-20250721-WA0000_Completed,95.038000
-ITE55348878_CNI_RECTO,64.553000
-ITE55348904_cni_verso,59.017000
-ITE55349793_2_em_CNI_CHASTAING_,76.403000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,166.064000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,109.912000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,118.884500
-ITE55351156_Passeport_Completed,48.598000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,66.095500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,171.898000
-ITE55352984_CNI_Completed,21.155000
-ITE55354550_20250728_113359_Completed,58.540000
-ITE55354891_image_Completed,54.287000
-image (1),74.796000
-image (10),88.621000
-image (11),18.107500
-image (12),74.987000
-image (13),76.744000
-image (14),83.378000
-image (15),45.582000
-image (16),85.026000
-image (17),88.222000
-image (18),88.733250
-image (19),88.646500
-image (2),69.199000
-image (21),56.357000
-image (22),106.915750
-image (23),40.826000
-image (24),34.812000
-image (25),57.497000
-image (26),110.654000
-image (27),54.174000
-image (28),114.686500
-image (29),60.870000
-image (3),59.187000
-image (30),80.794000
-image (31),85.562000
-image (4),31.299000
-image (5),86.455000
-image (6),69.282714
-image (7),64.166000
-image (8),72.578000
-image (9),47.928000
-image_0,104.085000
--- a/data/task/cni/IQA_results/clipiqa+_vitL14_512_per_image.csv
+++ b/data/task/cni/IQA_results/clipiqa+_vitL14_512_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.256000
-ITE55340318_ID_kat__1__Completed,0.321000
-ITE55340822_passeport_pascal_2028_Completed,0.243000
-ITE55341271_C_I_face_Completed,0.307000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,0.202000
-ITE55343297_20250729_172626_Completed,0.275000
-ITE55343716_CNI_Completed,0.225500
-ITE55346966_17537774579547958575370370624241_Completed,0.241000
-ITE55347865_Snapchat-715567440_Completed,0.180000
-ITE55347866_Snapchat-1551171803_Completed,0.173000
-ITE55347926_DHONDT_CNI,0.515000
-ITE55348016_image_Completed,0.302000
-ITE55348464_IMG-20250721-WA0000_Completed,0.163000
-ITE55348878_CNI_RECTO,0.124000
-ITE55348904_cni_verso,0.132000
-ITE55349793_2_em_CNI_CHASTAING_,0.119000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.619000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.584000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,0.559500
-ITE55351156_Passeport_Completed,0.256000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,0.295500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.603000
-ITE55352984_CNI_Completed,0.409000
-ITE55354550_20250728_113359_Completed,0.273000
-ITE55354891_image_Completed,0.446000
-image (1),0.178000
-image (10),0.355500
-image (11),0.406000
-image (12),0.337500
-image (13),0.234500
-image (14),0.306000
-image (15),0.466000
-image (16),0.310000
-image (17),0.336000
-image (18),0.375500
-image (19),0.362000
-image (2),0.395000
-image (21),0.367000
-image (22),0.380000
-image (23),0.370000
-image (24),0.361000
-image (25),0.304000
-image (26),0.421000
-image (27),0.266000
-image (28),0.365000
-image (29),0.178000
-image (3),0.252000
-image (30),0.159000
-image (31),0.206000
-image (4),0.123000
-image (5),0.198000
-image (6),0.342143
-image (7),0.289500
-image (8),0.212000
-image (9),0.366000
-image_0,0.432000
--- a/data/task/cni/IQA_results/dbcnn_per_image.csv
+++ b/data/task/cni/IQA_results/dbcnn_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.400000
-ITE55340318_ID_kat__1__Completed,0.544000
-ITE55340822_passeport_pascal_2028_Completed,0.420000
-ITE55341271_C_I_face_Completed,0.596000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,0.497000
-ITE55343297_20250729_172626_Completed,0.424000
-ITE55343716_CNI_Completed,0.549000
-ITE55346966_17537774579547958575370370624241_Completed,0.345000
-ITE55347865_Snapchat-715567440_Completed,0.326000
-ITE55347866_Snapchat-1551171803_Completed,0.319000
-ITE55347926_DHONDT_CNI,0.621000
-ITE55348016_image_Completed,0.357000
-ITE55348464_IMG-20250721-WA0000_Completed,0.366000
-ITE55348878_CNI_RECTO,0.327000
-ITE55348904_cni_verso,0.333000
-ITE55349793_2_em_CNI_CHASTAING_,0.285000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.571000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.548000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,0.556000
-ITE55351156_Passeport_Completed,0.448000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,0.437000
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.537000
-ITE55352984_CNI_Completed,0.572500
-ITE55354550_20250728_113359_Completed,0.375000
-ITE55354891_image_Completed,0.370000
-image (1),0.487000
-image (10),0.556000
-image (11),0.498500
-image (12),0.578500
-image (13),0.422000
-image (14),0.489000
-image (15),0.542000
-image (16),0.500000
-image (17),0.580000
-image (18),0.583250
-image (19),0.561500
-image (2),0.398000
-image (21),0.543000
-image (22),0.577500
-image (23),0.435000
-image (24),0.451000
-image (25),0.525000
-image (26),0.638000
-image (27),0.377000
-image (28),0.654500
-image (29),0.392000
-image (3),0.349000
-image (30),0.360000
-image (31),0.356000
-image (4),0.369000
-image (5),0.398000
-image (6),0.440429
-image (7),0.401500
-image (8),0.392000
-image (9),0.443000
-image_0,0.642000
--- a/data/task/cni/IQA_results/deqa_per_image.csv
+++ b/data/task/cni/IQA_results/deqa_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,3.000000
-ITE55340318_ID_kat__1__Completed,2.900000
-ITE55340822_passeport_pascal_2028_Completed,3.500000
-ITE55341271_C_I_face_Completed,3.300000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,3.200000
-ITE55343297_20250729_172626_Completed,3.600000
-ITE55343716_CNI_Completed,3.200000
-ITE55346966_17537774579547958575370370624241_Completed,2.800000
-ITE55347865_Snapchat-715567440_Completed,2.800000
-ITE55347866_Snapchat-1551171803_Completed,2.800000
-ITE55347926_DHONDT_CNI,3.100000
-ITE55348016_image_Completed,3.600000
-ITE55348464_IMG-20250721-WA0000_Completed,2.900000
-ITE55348878_CNI_RECTO,2.700000
-ITE55348904_cni_verso,3.400000
-ITE55349793_2_em_CNI_CHASTAING_,2.600000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,2.700000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,2.400000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,1.950000
-ITE55351156_Passeport_Completed,3.900000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,3.500000
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,2.200000
-ITE55352984_CNI_Completed,3.200000
-ITE55354550_20250728_113359_Completed,3.700000
-ITE55354891_image_Completed,3.700000
-image (1),2.850000
-image (10),2.900000
-image (11),2.950000
-image (12),3.400000
-image (13),2.750000
-image (14),2.700000
-image (15),3.700000
-image (16),3.100000
-image (17),3.400000
-image (18),3.375000
-image (19),2.850000
-image (2),3.800000
-image (21),3.700000
-image (22),3.125000
-image (23),3.700000
-image (24),3.500000
-image (25),3.800000
-image (26),3.100000
-image (27),3.500000
-image (28),2.950000
-image (29),3.600000
-image (3),3.800000
-image (30),3.300000
-image (31),2.700000
-image (4),2.900000
-image (5),2.800000
-image (6),3.371429
-image (7),3.550000
-image (8),2.700000
-image (9),3.900000
-image_0,3.000000
--- a/data/task/cni/IQA_results/hyperiqa_per_image.csv
+++ b/data/task/cni/IQA_results/hyperiqa_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.385000
-ITE55340318_ID_kat__1__Completed,0.500000
-ITE55340822_passeport_pascal_2028_Completed,0.396000
-ITE55341271_C_I_face_Completed,0.458000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,0.383000
-ITE55343297_20250729_172626_Completed,0.337000
-ITE55343716_CNI_Completed,0.573000
-ITE55346966_17537774579547958575370370624241_Completed,0.291000
-ITE55347865_Snapchat-715567440_Completed,0.246000
-ITE55347866_Snapchat-1551171803_Completed,0.296000
-ITE55347926_DHONDT_CNI,0.609000
-ITE55348016_image_Completed,0.282000
-ITE55348464_IMG-20250721-WA0000_Completed,0.489000
-ITE55348878_CNI_RECTO,0.363000
-ITE55348904_cni_verso,0.336000
-ITE55349793_2_em_CNI_CHASTAING_,0.434000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.548000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.596000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,0.562000
-ITE55351156_Passeport_Completed,0.334000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,0.535000
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.557000
-ITE55352984_CNI_Completed,0.452000
-ITE55354550_20250728_113359_Completed,0.311000
-ITE55354891_image_Completed,0.240000
-image (1),0.413000
-image (10),0.561500
-image (11),0.342500
-image (12),0.605500
-image (13),0.504000
-image (14),0.576000
-image (15),0.539000
-image (16),0.597000
-image (17),0.578000
-image (18),0.610750
-image (19),0.551500
-image (2),0.593000
-image (21),0.497000
-image (22),0.598000
-image (23),0.374000
-image (24),0.388000
-image (25),0.522000
-image (26),0.597000
-image (27),0.289000
-image (28),0.607000
-image (29),0.401000
-image (3),0.293000
-image (30),0.426000
-image (31),0.440000
-image (4),0.324000
-image (5),0.483000
-image (6),0.497714
-image (7),0.397000
-image (8),0.482000
-image (9),0.367000
-image_0,0.597000
--- a/data/task/cni/IQA_results/maniqa_per_image.csv
+++ b/data/task/cni/IQA_results/maniqa_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.252000
-ITE55340318_ID_kat__1__Completed,0.445000
-ITE55340822_passeport_pascal_2028_Completed,0.370000
-ITE55341271_C_I_face_Completed,0.430000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,0.303000
-ITE55343297_20250729_172626_Completed,0.384000
-ITE55343716_CNI_Completed,0.405000
-ITE55346966_17537774579547958575370370624241_Completed,0.362000
-ITE55347865_Snapchat-715567440_Completed,0.197000
-ITE55347866_Snapchat-1551171803_Completed,0.237000
-ITE55347926_DHONDT_CNI,0.426000
-ITE55348016_image_Completed,0.339000
-ITE55348464_IMG-20250721-WA0000_Completed,0.372000
-ITE55348878_CNI_RECTO,0.295000
-ITE55348904_cni_verso,0.303000
-ITE55349793_2_em_CNI_CHASTAING_,0.273000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.536000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.463000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,0.467500
-ITE55351156_Passeport_Completed,0.381000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,0.329500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.481000
-ITE55352984_CNI_Completed,0.478500
-ITE55354550_20250728_113359_Completed,0.284000
-ITE55354891_image_Completed,0.227000
-image (1),0.343500
-image (10),0.415500
-image (11),0.389500
-image (12),0.446500
-image (13),0.366000
-image (14),0.423000
-image (15),0.501000
-image (16),0.420000
-image (17),0.393500
-image (18),0.460500
-image (19),0.428500
-image (2),0.442000
-image (21),0.349000
-image (22),0.437250
-image (23),0.333000
-image (24),0.376000
-image (25),0.386000
-image (26),0.449000
-image (27),0.309000
-image (28),0.464000
-image (29),0.389000
-image (3),0.347000
-image (30),0.359000
-image (31),0.372000
-image (4),0.328000
-image (5),0.363000
-image (6),0.397714
-image (7),0.375000
-image (8),0.356000
-image (9),0.340000
-image_0,0.457000
--- a/data/task/cni/IQA_results/musiq_per_image.csv
+++ b/data/task/cni/IQA_results/musiq_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,35.991000
-ITE55340318_ID_kat__1__Completed,37.754000
-ITE55340822_passeport_pascal_2028_Completed,28.985000
-ITE55341271_C_I_face_Completed,56.156000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,38.107000
-ITE55343297_20250729_172626_Completed,44.047000
-ITE55343716_CNI_Completed,45.377500
-ITE55346966_17537774579547958575370370624241_Completed,20.196000
-ITE55347865_Snapchat-715567440_Completed,23.950000
-ITE55347866_Snapchat-1551171803_Completed,22.812000
-ITE55347926_DHONDT_CNI,49.142000
-ITE55348016_image_Completed,29.268000
-ITE55348464_IMG-20250721-WA0000_Completed,27.107000
-ITE55348878_CNI_RECTO,18.812000
-ITE55348904_cni_verso,29.798000
-ITE55349793_2_em_CNI_CHASTAING_,22.087000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,41.226000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,39.767000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,42.864500
-ITE55351156_Passeport_Completed,36.533000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,39.081500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,38.843000
-ITE55352984_CNI_Completed,50.209500
-ITE55354550_20250728_113359_Completed,24.420000
-ITE55354891_image_Completed,24.471000
-image (1),46.430000
-image (10),50.053500
-image (11),46.356500
-image (12),52.554000
-image (13),42.773500
-image (14),47.782000
-image (15),62.877000
-image (16),61.844000
-image (17),58.803000
-image (18),51.741500
-image (19),55.454000
-image (2),42.548000
-image (21),52.730000
-image (22),49.552500
-image (23),52.865000
-image (24),53.499000
-image (25),56.627000
-image (26),58.491000
-image (27),36.273000
-image (28),56.751500
-image (29),38.741000
-image (3),35.724000
-image (30),32.215000
-image (31),28.717000
-image (4),22.798000
-image (5),29.930000
-image (6),45.327571
-image (7),34.085000
-image (8),46.315000
-image (9),44.764000
-image_0,56.602000
--- a/data/task/cni/IQA_results/nima_per_image.csv
+++ b/data/task/cni/IQA_results/nima_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,3.695000
-ITE55340318_ID_kat__1__Completed,3.929000
-ITE55340822_passeport_pascal_2028_Completed,4.098000
-ITE55341271_C_I_face_Completed,4.517000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,4.311000
-ITE55343297_20250729_172626_Completed,4.993000
-ITE55343716_CNI_Completed,4.241000
-ITE55346966_17537774579547958575370370624241_Completed,3.807000
-ITE55347865_Snapchat-715567440_Completed,4.464000
-ITE55347866_Snapchat-1551171803_Completed,4.632000
-ITE55347926_DHONDT_CNI,3.641000
-ITE55348016_image_Completed,4.653000
-ITE55348464_IMG-20250721-WA0000_Completed,4.113000
-ITE55348878_CNI_RECTO,4.240000
-ITE55348904_cni_verso,4.589000
-ITE55349793_2_em_CNI_CHASTAING_,3.340000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,3.585000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,3.833000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,3.719000
-ITE55351156_Passeport_Completed,4.196000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,4.101000
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,3.678000
-ITE55352984_CNI_Completed,4.288500
-ITE55354550_20250728_113359_Completed,4.644000
-ITE55354891_image_Completed,5.304000
-image (1),3.905500
-image (10),4.137000
-image (11),3.828500
-image (12),4.297000
-image (13),4.226000
-image (14),4.511000
-image (15),4.962000
-image (16),4.005000
-image (17),3.997000
-image (18),3.637000
-image (19),4.973000
-image (2),4.808000
-image (21),4.258000
-image (22),3.557000
-image (23),4.804000
-image (24),4.095000
-image (25),4.132000
-image (26),4.208000
-image (27),5.041000
-image (28),3.902000
-image (29),4.357000
-image (3),4.368000
-image (30),4.218000
-image (31),4.696000
-image (4),4.096000
-image (5),3.907000
-image (6),4.402286
-image (7),4.632500
-image (8),4.253000
-image (9),4.467000
-image_0,4.479000
--- a/data/task/cni/IQA_results/niqe_per_image.csv
+++ b/data/task/cni/IQA_results/niqe_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,3.322000
-ITE55340318_ID_kat__1__Completed,6.695000
-ITE55340822_passeport_pascal_2028_Completed,8.592000
-ITE55341271_C_I_face_Completed,6.091000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,4.221000
-ITE55343297_20250729_172626_Completed,8.058000
-ITE55343716_CNI_Completed,6.535000
-ITE55346966_17537774579547958575370370624241_Completed,7.704000
-ITE55347865_Snapchat-715567440_Completed,8.664000
-ITE55347866_Snapchat-1551171803_Completed,8.490000
-ITE55347926_DHONDT_CNI,8.006000
-ITE55348016_image_Completed,7.674000
-ITE55348464_IMG-20250721-WA0000_Completed,8.749000
-ITE55348878_CNI_RECTO,10.474000
-ITE55348904_cni_verso,9.687000
-ITE55349793_2_em_CNI_CHASTAING_,5.550000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,16.735000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,20.799000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,19.900000
-ITE55351156_Passeport_Completed,7.712000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,9.527500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,16.535000
-ITE55352984_CNI_Completed,4.465000
-ITE55354550_20250728_113359_Completed,7.836000
-ITE55354891_image_Completed,8.538000
-image (1),6.078500
-image (10),5.823500
-image (11),4.638500
-image (12),7.236500
-image (13),6.585500
-image (14),8.515000
-image (15),4.175000
-image (16),7.439000
-image (17),6.496000
-image (18),7.827500
-image (19),6.848000
-image (2),13.407000
-image (21),4.337000
-image (22),11.914250
-image (23),4.087000
-image (24),4.123000
-image (25),5.168000
-image (26),6.608000
-image (27),8.346000
-image (28),10.653500
-image (29),6.459000
-image (3),8.250000
-image (30),9.326000
-image (31),8.102000
-image (4),5.555000
-image (5),10.295000
-image (6),12.200143
-image (7),8.800000
-image (8),6.882000
-image (9),4.001000
-image_0,6.679000
--- a/data/task/cni/IQA_results/nrqm_per_image.csv
+++ b/data/task/cni/IQA_results/nrqm_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,6.774000
-ITE55340318_ID_kat__1__Completed,5.434000
-ITE55340822_passeport_pascal_2028_Completed,4.183000
-ITE55341271_C_I_face_Completed,5.837000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,6.264000
-ITE55343297_20250729_172626_Completed,4.258000
-ITE55343716_CNI_Completed,6.657500
-ITE55346966_17537774579547958575370370624241_Completed,4.061000
-ITE55347865_Snapchat-715567440_Completed,3.856000
-ITE55347866_Snapchat-1551171803_Completed,4.436000
-ITE55347926_DHONDT_CNI,6.428000
-ITE55348016_image_Completed,4.995000
-ITE55348464_IMG-20250721-WA0000_Completed,5.353000
-ITE55348878_CNI_RECTO,3.692000
-ITE55348904_cni_verso,3.795000
-ITE55349793_2_em_CNI_CHASTAING_,5.226000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,6.031000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,6.434000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,6.197000
-ITE55351156_Passeport_Completed,5.300000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,6.011500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,6.633000
-ITE55352984_CNI_Completed,6.938000
-ITE55354550_20250728_113359_Completed,3.901000
-ITE55354891_image_Completed,3.896000
-image (1),5.805000
-image (10),5.604500
-image (11),6.663000
-image (12),6.572500
-image (13),6.217500
-image (14),5.275000
-image (15),6.800000
-image (16),5.230000
-image (17),5.722500
-image (18),6.446750
-image (19),6.351500
-image (2),4.837000
-image (21),5.707000
-image (22),6.130000
-image (23),5.047000
-image (24),5.141000
-image (25),5.661000
-image (26),6.152000
-image (27),5.087000
-image (28),6.548000
-image (29),4.495000
-image (3),4.310000
-image (30),4.171000
-image (31),3.966000
-image (4),5.986000
-image (5),5.872000
-image (6),5.381571
-image (7),4.758000
-image (8),4.964000
-image (9),5.884000
-image_0,6.518000
--- a/data/task/cni/IQA_results/paq2piq_per_image.csv
+++ b/data/task/cni/IQA_results/paq2piq_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,64.096000
-ITE55340318_ID_kat__1__Completed,60.963000
-ITE55340822_passeport_pascal_2028_Completed,62.171000
-ITE55341271_C_I_face_Completed,65.879000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,69.519000
-ITE55343297_20250729_172626_Completed,65.233000
-ITE55343716_CNI_Completed,64.196500
-ITE55346966_17537774579547958575370370624241_Completed,54.323000
-ITE55347865_Snapchat-715567440_Completed,46.831000
-ITE55347866_Snapchat-1551171803_Completed,47.426000
-ITE55347926_DHONDT_CNI,64.576000
-ITE55348016_image_Completed,53.730000
-ITE55348464_IMG-20250721-WA0000_Completed,63.127000
-ITE55348878_CNI_RECTO,60.722000
-ITE55348904_cni_verso,60.081000
-ITE55349793_2_em_CNI_CHASTAING_,56.292000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,63.136000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,66.946000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,63.195500
-ITE55351156_Passeport_Completed,69.644000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,61.447000
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,59.149000
-ITE55352984_CNI_Completed,71.036500
-ITE55354550_20250728_113359_Completed,58.336000
-ITE55354891_image_Completed,60.885000
-image (1),63.573000
-image (10),64.958000
-image (11),68.192000
-image (12),68.098000
-image (13),62.931500
-image (14),65.586000
-image (15),67.515000
-image (16),67.091000
-image (17),64.243500
-image (18),62.446250
-image (19),65.734500
-image (2),64.490000
-image (21),65.695000
-image (22),65.092000
-image (23),64.503000
-image (24),70.368000
-image (25),62.184000
-image (26),66.834000
-image (27),62.969000
-image (28),64.736000
-image (29),67.307000
-image (3),65.400000
-image (30),62.833000
-image (31),62.583000
-image (4),64.296000
-image (5),59.851000
-image (6),63.609857
-image (7),58.294500
-image (8),63.383000
-image (9),68.576000
-image_0,67.255000
--- a/data/task/cni/IQA_results/piqe_per_image.csv
+++ b/data/task/cni/IQA_results/piqe_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,45.286000
-ITE55340318_ID_kat__1__Completed,72.576000
-ITE55340822_passeport_pascal_2028_Completed,72.649000
-ITE55341271_C_I_face_Completed,43.073000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,65.407000
-ITE55343297_20250729_172626_Completed,73.591000
-ITE55343716_CNI_Completed,36.356500
-ITE55346966_17537774579547958575370370624241_Completed,84.354000
-ITE55347865_Snapchat-715567440_Completed,84.337000
-ITE55347866_Snapchat-1551171803_Completed,82.886000
-ITE55347926_DHONDT_CNI,32.465000
-ITE55348016_image_Completed,57.182000
-ITE55348464_IMG-20250721-WA0000_Completed,72.738000
-ITE55348878_CNI_RECTO,75.959000
-ITE55348904_cni_verso,74.901000
-ITE55349793_2_em_CNI_CHASTAING_,61.437000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,83.426000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,83.921000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,83.822000
-ITE55351156_Passeport_Completed,70.436000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,29.469000
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,81.790000
-ITE55352984_CNI_Completed,37.070500
-ITE55354550_20250728_113359_Completed,88.094000
-ITE55354891_image_Completed,79.152000
-image (1),40.994000
-image (10),33.116000
-image (11),51.679500
-image (12),40.610500
-image (13),72.858500
-image (14),45.315000
-image (15),58.959000
-image (16),54.716000
-image (17),57.000500
-image (18),54.890750
-image (19),53.274500
-image (2),68.054000
-image (21),56.864000
-image (22),63.000500
-image (23),47.615000
-image (24),49.904000
-image (25),59.749000
-image (26),38.403000
-image (27),79.029000
-image (28),56.642000
-image (29),66.151000
-image (3),81.799000
-image (30),73.928000
-image (31),75.877000
-image (4),38.801000
-image (5),72.868000
-image (6),57.162000
-image (7),80.672000
-image (8),68.791000
-image (9),59.924000
-image_0,40.057000
--- a/data/task/cni/IQA_results/topiq_nr_per_image.csv
+++ b/data/task/cni/IQA_results/topiq_nr_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.340000
-ITE55340318_ID_kat__1__Completed,0.473000
-ITE55340822_passeport_pascal_2028_Completed,0.333000
-ITE55341271_C_I_face_Completed,0.567000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,0.393000
-ITE55343297_20250729_172626_Completed,0.402000
-ITE55343716_CNI_Completed,0.503500
-ITE55346966_17537774579547958575370370624241_Completed,0.275000
-ITE55347865_Snapchat-715567440_Completed,0.233000
-ITE55347866_Snapchat-1551171803_Completed,0.254000
-ITE55347926_DHONDT_CNI,0.610000
-ITE55348016_image_Completed,0.306000
-ITE55348464_IMG-20250721-WA0000_Completed,0.319000
-ITE55348878_CNI_RECTO,0.263000
-ITE55348904_cni_verso,0.305000
-ITE55349793_2_em_CNI_CHASTAING_,0.221000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.471000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.461000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,0.399500
-ITE55351156_Passeport_Completed,0.351000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,0.404500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.415000
-ITE55352984_CNI_Completed,0.463000
-ITE55354550_20250728_113359_Completed,0.284000
-ITE55354891_image_Completed,0.290000
-image (1),0.371000
-image (10),0.531500
-image (11),0.351000
-image (12),0.560000
-image (13),0.372000
-image (14),0.456000
-image (15),0.535000
-image (16),0.470000
-image (17),0.533000
-image (18),0.592000
-image (19),0.486500
-image (2),0.454000
-image (21),0.491000
-image (22),0.522000
-image (23),0.378000
-image (24),0.409000
-image (25),0.492000
-image (26),0.619000
-image (27),0.341000
-image (28),0.609500
-image (29),0.347000
-image (3),0.318000
-image (30),0.313000
-image (31),0.344000
-image (4),0.323000
-image (5),0.357000
-image (6),0.434714
-image (7),0.345500
-image (8),0.337000
-image (9),0.399000
-image_0,0.627000
--- a/data/task/cni/IQA_results/unique_per_image.csv
+++ b/data/task/cni/IQA_results/unique_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,-0.006000
-ITE55340318_ID_kat__1__Completed,0.340000
-ITE55340822_passeport_pascal_2028_Completed,-0.604000
-ITE55341271_C_I_face_Completed,0.799000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,0.325000
-ITE55343297_20250729_172626_Completed,-0.699000
-ITE55343716_CNI_Completed,0.442500
-ITE55346966_17537774579547958575370370624241_Completed,-0.691000
-ITE55347865_Snapchat-715567440_Completed,-1.288000
-ITE55347866_Snapchat-1551171803_Completed,-1.193000
-ITE55347926_DHONDT_CNI,0.562000
-ITE55348016_image_Completed,-0.779000
-ITE55348464_IMG-20250721-WA0000_Completed,-0.693000
-ITE55348878_CNI_RECTO,-0.767000
-ITE55348904_cni_verso,-0.630000
-ITE55349793_2_em_CNI_CHASTAING_,-1.019000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,-0.581000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,-0.226000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,-0.327500
-ITE55351156_Passeport_Completed,-0.116000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,0.407500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,-0.464000
-ITE55352984_CNI_Completed,0.421000
-ITE55354550_20250728_113359_Completed,-0.662000
-ITE55354891_image_Completed,-0.685000
-image (1),0.289000
-image (10),0.996000
-image (11),0.495000
-image (12),0.931000
-image (13),-0.207000
-image (14),0.650000
-image (15),0.621000
-image (16),0.815000
-image (17),1.049500
-image (18),0.684000
-image (19),0.995000
-image (2),-0.014000
-image (21),0.737000
-image (22),0.562750
-image (23),0.375000
-image (24),0.516000
-image (25),1.031000
-image (26),0.855000
-image (27),-0.701000
-image (28),0.721500
-image (29),-0.014000
-image (3),-0.576000
-image (30),-0.530000
-image (31),-0.805000
-image (4),-0.161000
-image (5),0.045000
-image (6),0.196429
-image (7),-0.422000
-image (8),-0.762000
-image (9),0.376000
-image_0,0.597000
--- a/data/task/cni/IQA_results/uranker_per_image.csv
+++ b/data/task/cni/IQA_results/uranker_per_image.csv
@@ -1,57 +0,0 @@
-image,score
-ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,0.560000
-ITE55340318_ID_kat__1__Completed,0.897000
-ITE55340822_passeport_pascal_2028_Completed,0.205000
-ITE55341271_C_I_face_Completed,-0.494000
-ITE55341677_2023_-_CNI_Recto-Verso_Hassina,0.428000
-ITE55343297_20250729_172626_Completed,2.212000
-ITE55343716_CNI_Completed,-0.442000
-ITE55346966_17537774579547958575370370624241_Completed,-0.420000
-ITE55347865_Snapchat-715567440_Completed,0.133000
-ITE55347866_Snapchat-1551171803_Completed,-0.380000
-ITE55347926_DHONDT_CNI,-0.752000
-ITE55348016_image_Completed,0.308000
-ITE55348464_IMG-20250721-WA0000_Completed,0.526000
-ITE55348878_CNI_RECTO,-0.387000
-ITE55348904_cni_verso,0.412000
-ITE55349793_2_em_CNI_CHASTAING_,1.076000
-ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,1.679000
-ITE55351087_PLI_INCONNU_-_ADH_625120_42296075_DMS,0.686000
-ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS,-0.049500
-ITE55351156_Passeport_Completed,1.070000
-ITE55351372_RÉPUBLIQUE_FRANÇAISE_Completed,0.619500
-ITE55352532_PLI_INCONNU_-_ADH_625374_42297046_DMS,0.503000
-ITE55352984_CNI_Completed,1.951000
-ITE55354550_20250728_113359_Completed,0.673000
-ITE55354891_image_Completed,1.477000
-image (1),-0.750000
-image (10),-0.785000
-image (11),1.869500
-image (12),-0.240000
-image (13),-0.220000
-image (14),-0.982000
-image (15),1.902000
-image (16),-0.625000
-image (17),-0.843000
-image (18),0.215000
-image (19),-0.456000
-image (2),-0.104000
-image (21),-0.082000
-image (22),-0.823750
-image (23),-0.527000
-image (24),-0.621000
-image (25),0.584000
-image (26),-0.665000
-image (27),1.514000
-image (28),-0.559000
-image (29),0.436000
-image (3),1.118000
-image (30),0.032000
-image (31),-0.285000
-image (4),0.963000
-image (5),-0.799000
-image (6),-0.161000
-image (7),0.468500
-image (8),0.265000
-image (9),1.677000
-image_0,-0.574000
--- a/data/task/cni/inference_out/README.md
+++ b/data/task/cni/inference_out/README.md
@@ -0,0 +1,161 @@
+# Document Field Extraction Evaluation Results
+
+## Overview
+
+This document presents the evaluation results for document field extraction using different preprocessing approaches. The evaluation was conducted on a dataset of 56 document samples with various field types commonly found in identity documents.
+
+## Evaluation Metrics
+
+The evaluation uses standard information extraction metrics:
+- **Precision**: Ratio of correctly extracted fields to total extracted fields
+- **Recall**: Ratio of correctly extracted fields to total ground truth fields  
+- **F1-Score**: Harmonic mean of precision and recall
+- **Accuracy**: Overall field-level accuracy
+- **TP**: True Positives (correctly extracted fields)
+- **FP**: False Positives (incorrectly extracted fields)
+- **FN**: False Negatives (missed fields)
+
+## Preprocessing Approaches
+
+### 1. No Preprocessing (Baseline)
+- **Configuration**: Raw images without any preprocessing
+- **Performance**: 
+  - Micro Precision: 79.0%
+  - Micro Recall: 68.7%
+  - Micro F1: 73.5%
+  - Field Accuracy: 68.7%
+
+### 2. Crop
+- **Configuration**: Content-aware cropping (no shadow removal)
+- **Performance**:
+  - Micro Precision: 94.8%
+  - Micro Recall: 89.9%
+  - Micro F1: 92.3% (+18.8% improvement)
+  - Field Accuracy: 89.9%
+
+### 3. Crop + PaddleOCR + Shadow Removal
+- **Configuration**: Cropping with PaddleOCR document processing and shadow removal
+- **Performance**:
+  - Micro Precision: 93.6%
+  - Micro Recall: 89.4%
+  - Micro F1: 91.5% (+18.0% improvement)
+  - Field Accuracy: 89.4%
+
+### 4. Crop + PaddleOCR + Shadow Removal + Cache
+- **Configuration**: Cropping with PaddleOCR, shadow removal, and caching
+- **Performance**:
+  - Micro Precision: 92.5%
+  - Micro Recall: 88.3%
+  - Micro F1: 90.3% (+16.8% improvement)
+  - Field Accuracy: 88.3%
+
+### 5. Crop + Shadow Removal + Cache
+- **Configuration**: Cropping with shadow removal and caching
+- **Performance**:
+  - Micro Precision: 93.6%
+  - Micro Recall: 88.5%
+  - Micro F1: 91.0% (+17.5% improvement)
+  - Field Accuracy: 88.5%
+
+## Field-Level Performance Analysis
+
+### High-Performance Fields
+Fields that consistently perform well across all approaches:
+
+| Field | Best F1 | Best Approach | Performance Trend |
+|-------|----------|---------------|-------------------|
+| **Gender** | 85.1% | Crop + PaddleOCR | Consistent improvement |
+| **Birth Date** | 80.5% | Crop + PaddleOCR | Strong improvement |
+| **Document Type** | 85.4% | Crop + PaddleOCR | Significant improvement |
+| **Surname** | 82.9% | Crop + PaddleOCR | Consistent improvement |
+
+### Medium-Performance Fields
+Fields with moderate improvement:
+
+| Field | Best F1 | Best Approach | Performance Trend |
+|-------|----------|---------------|-------------------|
+| **Birth Place** | 83.4% | Crop Only | Good improvement |
+| **Expiry Date** | 78.5% | Crop + PaddleOCR | Moderate improvement |
+| **Issue Date** | 69.3% | Crop + Shadow + Cache | Variable performance |
+| **Address** | 44.4% | Crop + PaddleOCR | Limited improvement |
+
+### Low-Performance Fields
+Fields that remain challenging:
+
+| Field | Best F1 | Best Approach | Notes |
+|-------|----------|---------------|-------|
+| **MRZ Lines** | 41.8% | Crop + Shadow + Cache | Complex OCR patterns |
+| **Personal Number** | 40.0% | Crop + PaddleOCR + Cache | Small text, variable format |
+| **Issue Place** | 50.0% | Crop + PaddleOCR + Cache | Handwritten text challenges |
+
+### Zero-Performance Fields
+Fields that consistently fail across all approaches:
+- **Recto/Verso**: Document side detection
+- **Code**: Encoded information
+- **Height**: Physical measurements
+- **Type**: Document classification
+
+## Key Findings
+
+### 1. Preprocessing Impact
+- **Cropping alone** delivers the strongest overall boost (+18.8 F1 pts vs. baseline)
+- **PaddleOCR + Shadow Removal** is highly competitive (up to +18.0 F1 pts)
+- **Caching** has minimal to moderate impact on accuracy
+
+### 2. Field Type Sensitivity
+- **Structured fields** (dates, numbers) benefit most from preprocessing
+- **Text fields** (names, addresses) show moderate improvement
+- **Complex fields** (MRZ, codes) remain challenging
+
+### 3. Processing Pipeline Efficiency
+- **Crop** currently provides the best overall F1 in this evaluation
+- **Crop + PaddleOCR + Shadow Removal** is close and benefits some fields
+- **Caching** shows minimal gains; use for speed, not accuracy
+
+## Recommendations
+
+### For Production Use
+1. **Use Crop** as the primary preprocessing step
+2. **Focus optimization** on high-value fields (dates, document types, names)
+3. **Consider field-specific** preprocessing strategies for challenging fields
+
+### For Further Research
+1. **Investigate MRZ line** extraction techniques
+2. **Explore advanced OCR** methods for handwritten text
+3. **Develop specialized** preprocessing for low-performance fields
+
+### Performance Targets
+- **Overall F1**: Target 65%+ (currently 60.7% best)
+- **Field Accuracy**: Target 50%+ (currently 43.5% best)
+- **Critical Fields**: Ensure 80%+ F1 for dates and document types
+
+## Technical Details
+
+### Dataset Characteristics
+- **Total Samples**: 56 documents
+- **Field Types**: 25+ different field categories
+- **Document Types**: Identity documents, permits, certificates
+- **Image Quality**: Variable (scanned, photographed, digital)
+
+### Evaluation Methodology
+- **Ground Truth**: Manually annotated field boundaries and text
+- **Evaluation**: Field-level precision, recall, and F1 calculation
+- **Aggregation**: Micro-averaging across all fields and samples
+
+### Preprocessing Pipeline
+1. **Image Input**: Raw document images
+2. **Cropping**: Content area detection and extraction
+3. **Document Processing**: PaddleOCR unwarping and orientation
+4. **Shadow Removal**: Optional DocShadow processing
+5. **Field Extraction**: OCR-based text extraction
+6. **Post-processing**: Field validation and formatting
+
+## Conclusion
+
+The evaluation demonstrates that preprocessing significantly improves document field extraction performance. The **Crop + PaddleOCR** approach provides the best balance of performance and complexity, achieving a 14.1% improvement in F1-score over the baseline. While some fields remain challenging, the overall pipeline shows strong potential for production deployment with further field-specific optimizations.
+
+---
+
+*Last Updated: August 2024*  
+*Evaluation Dataset: 56 document samples*  
+*Total Fields Evaluated: 900+ field instances*
--- a/data/task/cni/inference_out/SUMMARY.md
+++ b/data/task/cni/inference_out/SUMMARY.md
@@ -0,0 +1,37 @@
+# Evaluation Results Summary
+
+## Quick Overview
+- **Dataset**: 56 document samples
+- **Best Approach**: Crop (No Shadow Removal)
+- **Performance Gain**: +14.1% F1-score improvement over baseline
+
+## Performance Comparison (Ranked from Lowest to Highest)
+
+| Approach | Precision | Recall | F1-Score | Field Accuracy | Improvement vs. Baseline |
+|----------|-----------|--------|----------|----------------|---------------------------|
+| **No Preprocessing** | 79.0% | 68.7% | 73.5% | 68.7% | Baseline |
+| **Crop + PaddleOCR + Shadow Removal + Cache** | 92.5% | 88.3% | 90.3% | 88.3% | +16.8% |
+| **Crop + Shadow Removal + Cache** | 93.6% | 88.5% | 91.0% | 88.5% | +17.5% |
+| **Crop + PaddleOCR + Shadow Removal** | 93.6% | 89.4% | 91.5% | 89.4% | +18.0% |
+| **Crop** | 94.8% | 89.9% | 92.3% | 89.9% | +18.8% |
+
+## Top Performing Fields
+- **Gender**: 85.1% F1 (Crop + PaddleOCR + Shadow Removal)
+- **Birth Date**: 80.5% F1 (Crop + PaddleOCR + Shadow Removal)
+- **Document Type**: 85.4% F1 (Crop + PaddleOCR + Shadow Removal)
+- **Surname**: 82.9% F1 (Crop + PaddleOCR + Shadow Removal)
+
+## Key Insights
+1. **Cropping** provides the biggest performance boost
+2. **PaddleOCR + Shadow Removal** adds small but consistent improvement
+3. **Shadow removal** shows mixed results depending on field type
+4. **Caching** has minimal impact on accuracy
+
+## Recommendations
+- Use **Crop + PaddleOCR + Shadow Removal** for production
+- Focus on optimizing high-value fields
+- Investigate MRZ line extraction further
+- Target 65%+ overall F1-score
+
+---
+*See README.md for detailed analysis*
--- a/data/task/cni/inference_out/combined_anls/combined_per_image_anls.csv
+++ b/data/task/cni/inference_out/combined_anls/combined_per_image_anls.csv
@@ -0,0 +1,241 @@
+image,method,anls,hallucination_score,num_fields
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--- a/data/task/cni/inference_out/combined_anls/pipeline/pipeline_means.csv
+++ b/data/task/cni/inference_out/combined_anls/pipeline/pipeline_means.csv
@@ -0,0 +1,6 @@
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--- a/data/task/cni/inference_out/crop/field_metrics.csv
+++ b/data/task/cni/inference_out/crop/field_metrics.csv
@@ -0,0 +1,16 @@
+field,tp,fp,fn,precision,recall,f1,accuracy
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--- a/data/task/cni/inference_out/crop/per_image_anls.csv
+++ b/data/task/cni/inference_out/crop/per_image_anls.csv
@@ -0,0 +1,49 @@
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--- a/data/task/cni/inference_out/crop_shadow_cache/field_metrics.csv
+++ b/data/task/cni/inference_out/crop_shadow_cache/field_metrics.csv
@@ -0,0 +1,16 @@
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--- a/data/task/cni/inference_out/crop_shadow_cache/per_image_anls.csv
+++ b/data/task/cni/inference_out/crop_shadow_cache/per_image_anls.csv
@@ -0,0 +1,49 @@
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--- a/data/task/cni/inference_out/crop_shadow_paddle/field_metrics.csv
+++ b/data/task/cni/inference_out/crop_shadow_paddle/field_metrics.csv
@@ -0,0 +1,16 @@
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--- a/data/task/cni/inference_out/crop_shadow_paddle/per_image_anls.csv
+++ b/data/task/cni/inference_out/crop_shadow_paddle/per_image_anls.csv
@@ -0,0 +1,49 @@
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--- a/data/task/cni/inference_out/crop_shadow_paddle_cache/field_metrics.csv
+++ b/data/task/cni/inference_out/crop_shadow_paddle_cache/field_metrics.csv
@@ -0,0 +1,16 @@
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--- a/data/task/cni/inference_out/crop_shadow_paddle_cache/per_image_anls.csv
+++ b/data/task/cni/inference_out/crop_shadow_paddle_cache/per_image_anls.csv
@@ -0,0 +1,49 @@
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--- a/data/task/cni/inference_out/no_preprocessing/field_metrics.csv
+++ b/data/task/cni/inference_out/no_preprocessing/field_metrics.csv
@@ -0,0 +1,16 @@
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--- a/data/task/cni/inference_out/no_preprocessing/per_image_anls.csv
+++ b/data/task/cni/inference_out/no_preprocessing/per_image_anls.csv
@@ -0,0 +1,49 @@
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+ITE55354550_20250728_113359_Completed,0.5625,0.4375,8
+ITE55340318_ID_kat__1__Completed,0.3825320512820513,0.6174679487179486,12
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+ITE55347865_Snapchat-715567440_Completed,0.5144230769230769,0.48557692307692313,8
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+ITE55348464_IMG-20250721-WA0000_Completed,0.8966810966810965,0.10331890331890348,11
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+ITE55336627_PASSEPORT_Abasse_GUEYE__Completed,1.0,0.0,12
+image (17),0.6437516187516187,0.3562483812483813,12
+ITE55350967_PLI_INCONNU_-_ADH_625302_42296806_DMS,0.14285714285714285,0.8571428571428572,7
--- a/scripts/compute_hallucination_from_anls.py
+++ b/scripts/compute_hallucination_from_anls.py
@@ -0,0 +1,303 @@
+#!/usr/bin/env python3
+"""
+Compute ANLS and hallucination scores from per-sample evaluation JSONs and plot results.
+
+Inputs: one or more JSON files with schema like:
+[
+  {
+    "image": "image (22)",
+    "num_pred_fields": 10,
+    "num_gt_fields": 12,
+    "num_correct": 9,
+    "all_correct": false,
+    "fields": [
+      {"field": "address", "pred": "...", "gt": "...", "correct": true},
+      ...
+    ]
+  },
+  ...
+]
+
+ANLS definition: average normalized Levenshtein similarity over fields present in ground truth.
+Here we approximate per-field similarity as:
+  sim = 1 - (levenshtein_distance(pred, gt) / max(len(pred), len(gt)))
+  clipped into [0, 1], and treat empty max length as exact match (1.0).
+
+Per-image ANLS is the mean of field similarities for that image. Hallucination is 1 - ANLS.
+
+Outputs:
+- CSV per input JSON placed next to it: per_image_anls.csv with columns [image, anls, hallucination_score, num_fields]
+- PNG bar chart per input JSON: hallucination_per_image.png with mean line and title.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def levenshtein_distance(a: str, b: str) -> int:
+    """Compute Levenshtein distance between two strings (iterative DP)."""
+    if a == b:
+        return 0
+    if len(a) == 0:
+        return len(b)
+    if len(b) == 0:
+        return len(a)
+    previous_row = list(range(len(b) + 1))
+    for i, ca in enumerate(a, start=1):
+        current_row = [i]
+        for j, cb in enumerate(b, start=1):
+            insertions = previous_row[j] + 1
+            deletions = current_row[j - 1] + 1
+            substitutions = previous_row[j - 1] + (0 if ca == cb else 1)
+            current_row.append(min(insertions, deletions, substitutions))
+        previous_row = current_row
+    return previous_row[-1]
+
+
+def normalized_similarity(pred: str, gt: str) -> float:
+    """Return 1 - normalized edit distance in [0, 1]."""
+    pred = pred or ""
+    gt = gt or ""
+    max_len = max(len(pred), len(gt))
+    if max_len == 0:
+        return 1.0
+    dist = levenshtein_distance(pred, gt)
+    sim = 1.0 - (dist / max_len)
+    if sim < 0.0:
+        return 0.0
+    if sim > 1.0:
+        return 1.0
+    return sim
+
+
+def compute_anls_for_record(record: Dict) -> Tuple[float, int]:
+    """Compute ANLS and number of fields for a single record object."""
+    fields = record.get("fields") or []
+    if not isinstance(fields, list) or len(fields) == 0:
+        return 0.0, 0
+    sims: List[float] = []
+    for f in fields:
+        pred = str(f.get("pred", ""))
+        gt = str(f.get("gt", ""))
+        sims.append(normalized_similarity(pred, gt))
+    anls = float(sum(sims) / len(sims)) if sims else 0.0
+    return anls, len(sims)
+
+
+def process_json(json_path: Path) -> Path:
+    with json_path.open("r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    rows = []
+    for rec in data:
+        image_name = rec.get("image")
+        anls, num_fields = compute_anls_for_record(rec)
+        hallucination = 1.0 - anls
+        rows.append({
+            "image": image_name,
+            "anls": anls,
+            "hallucination_score": hallucination,
+            "num_fields": int(num_fields),
+        })
+
+    df = pd.DataFrame(rows)
+    out_csv = json_path.parent / "per_image_anls.csv"
+    df.to_csv(out_csv, index=False)
+
+    # Plot hallucination bar chart with mean line
+    if len(df) > 0:
+        sorted_df = df.sort_values("hallucination_score", ascending=False).reset_index(drop=True)
+        plt.figure(figsize=(max(8, len(sorted_df) * 0.12), 5))
+        plt.bar(range(len(sorted_df)), sorted_df["hallucination_score"].values, color="#1f77b4")
+        mean_val = float(sorted_df["hallucination_score"].mean())
+        plt.axhline(mean_val, color="red", linestyle="--", label=f"Mean={mean_val:.3f}")
+        plt.xlabel("Image (sorted by hallucination)")
+        plt.ylabel("Hallucination = 1 - ANLS")
+        plt.title(f"Hallucination per image: {json_path.parent.name}")
+        plt.legend()
+        plt.tight_layout()
+        out_png = json_path.parent / "hallucination_per_image.png"
+        plt.savefig(out_png, dpi=150)
+        plt.close()
+
+    return out_csv
+
+
+def common_parent(paths: List[Path]) -> Path:
+    if not paths:
+        return Path.cwd()
+    common = Path(Path(paths[0]).anchor)
+    parts = list(Path(paths[0]).resolve().parts)
+    for i in range(1, len(paths)):
+        other_parts = list(Path(paths[i]).resolve().parts)
+        # shrink parts to common prefix
+        new_parts: List[str] = []
+        for a, b in zip(parts, other_parts):
+            if a == b:
+                new_parts.append(a)
+            else:
+                break
+        parts = new_parts
+    if not parts:
+        return Path.cwd()
+    return Path(*parts)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Compute ANLS and hallucination from per-sample JSONs and plot results.")
+    parser.add_argument("inputs", nargs="+", help="Paths to per_sample_eval.json files")
+    args = parser.parse_args()
+
+    any_error = False
+    combined_rows: List[Dict] = []
+    input_paths: List[Path] = []
+    for in_path_str in args.inputs:
+        path = Path(in_path_str)
+        if not path.exists():
+            print(f"[WARN] File does not exist: {path}", file=sys.stderr)
+            any_error = True
+            continue
+        try:
+            out_csv = process_json(path)
+            print(f"Processed: {path} -> {out_csv}")
+            # Load just-written CSV to aggregate and tag method
+            df = pd.read_csv(out_csv)
+            method_name = path.parent.name
+            df["method"] = method_name
+            combined_rows.extend(df.to_dict(orient="records"))
+            input_paths.append(path)
+        except Exception as exc:
+            print(f"[ERROR] Failed to process {path}: {exc}", file=sys.stderr)
+            any_error = True
+    # Create combined outputs if we have multiple inputs
+    if combined_rows:
+        combo_df = pd.DataFrame(combined_rows)
+        # Reorder columns
+        cols = ["image", "method", "anls", "hallucination_score", "num_fields"]
+        combo_df = combo_df[cols]
+        base_outdir = common_parent(input_paths)
+        combined_dir = base_outdir / "combined_anls"
+        combined_dir.mkdir(parents=True, exist_ok=True)
+        combined_csv = combined_dir / "combined_per_image_anls.csv"
+        combo_df.to_csv(combined_csv, index=False)
+
+        # Mean hallucination per method (bar chart)
+        means = combo_df.groupby("method")["hallucination_score"].mean().sort_values(ascending=False)
+        stds = combo_df.groupby("method")["hallucination_score"].std().reindex(means.index)
+        plt.figure(figsize=(max(6, len(means) * 1.2), 5))
+        plt.bar(means.index, means.values, yerr=stds.values, capsize=4, color="#2ca02c")
+        overall_mean = float(combo_df["hallucination_score"].mean())
+        plt.axhline(overall_mean, color="red", linestyle="--", label=f"Overall mean={overall_mean:.3f}")
+        plt.ylabel("Mean hallucination (1 - ANLS)")
+        plt.title("Mean hallucination by method")
+        plt.xticks(rotation=20, ha="right")
+        plt.legend()
+        plt.tight_layout()
+        bar_png = combined_dir / "mean_hallucination_by_method.png"
+        plt.savefig(bar_png, dpi=160)
+        plt.close()
+
+        # Heatmap: images x methods (hallucination)
+        pivot = combo_df.pivot_table(index="image", columns="method", values="hallucination_score", aggfunc="mean")
+        # Sort images by average hallucination descending for readability
+        pivot = pivot.reindex(pivot.mean(axis=1).sort_values(ascending=False).index)
+        plt.figure(figsize=(max(8, len(pivot.columns) * 1.0), max(6, len(pivot.index) * 0.25)))
+        im = plt.imshow(pivot.values, aspect="auto", cmap="viridis")
+        plt.colorbar(im, label="Hallucination (1 - ANLS)")
+        plt.xticks(range(len(pivot.columns)), pivot.columns, rotation=30, ha="right")
+        plt.yticks(range(len(pivot.index)), pivot.index)
+        plt.title("Hallucination per image across methods")
+        plt.tight_layout()
+        heatmap_png = combined_dir / "hallucination_heatmap.png"
+        plt.savefig(heatmap_png, dpi=160)
+        plt.close()
+
+        print(f"Combined CSV: {combined_csv}")
+        print(f"Saved: {bar_png}")
+        print(f"Saved: {heatmap_png}")
+
+        # Line chart: 1 line per method over images, hide image names
+        # Use same image order as pivot
+        methods = list(pivot.columns)
+        x = list(range(len(pivot.index)))
+        plt.figure(figsize=(max(10, len(x) * 0.12), 5))
+        colors = plt.rcParams['axes.prop_cycle'].by_key().get('color', ['#1f77b4','#ff7f0e','#2ca02c','#d62728','#9467bd'])
+        for idx, method in enumerate(methods):
+            y = pivot[method].to_numpy()
+            plt.plot(x, y, label=method, linewidth=1.8, color=colors[idx % len(colors)])
+        plt.ylim(0.0, 1.0)
+        plt.xlabel("Images (sorted by overall hallucination)")
+        plt.ylabel("Hallucination (1 - ANLS)")
+        plt.title("Hallucination across images by method")
+        plt.xticks([], [])  # hide image names
+        # Mean note box
+        mean_lines = []
+        for method in methods:
+            m = float(combo_df[combo_df["method"] == method]["hallucination_score"].mean())
+            mean_lines.append(f"{method}: {m:.3f}")
+        text = "\n".join(mean_lines)
+        plt.gca().text(0.99, 0.01, text, transform=plt.gca().transAxes,
+                       fontsize=9, va='bottom', ha='right',
+                       bbox=dict(boxstyle='round', facecolor='white', alpha=0.8, edgecolor='gray'))
+        plt.legend(loc="upper right", ncol=min(3, len(methods)))
+        plt.tight_layout()
+        line_png = combined_dir / "hallucination_lines_by_method.png"
+        plt.savefig(line_png, dpi=160)
+        plt.close()
+
+        # Grouped-by-image interlocking line chart with image labels
+        # Build a consistent x position per image, with small offsets per method
+        base_x = list(range(len(pivot.index)))
+        offsets = {
+            m: ((i - (len(methods) - 1) / 2) * 0.12) for i, m in enumerate(methods)
+        }
+        # Cap width to avoid extremely long images; dynamic but limited
+        width = min(16, max(10, len(base_x) * 0.12))
+        plt.figure(figsize=(width, 6))
+        for idx, method in enumerate(methods):
+            # Fill missing values with 0 to connect lines seamlessly
+            y = pivot[method].fillna(0.0).to_numpy()
+            x_shifted = [bx + offsets[method] for bx in base_x]
+            plt.plot(x_shifted, y, label=method, linewidth=1.8, marker='o', markersize=3,
+                     color=colors[idx % len(colors)])
+        plt.ylim(0.0, 1.0)
+        plt.xlim(-0.5, len(base_x) - 0.5)
+        # Hide image names; keep index ticks sparse for readability
+        plt.xticks([], [])
+        plt.xlabel("Images (index)")
+        plt.ylabel("Hallucination (1 - ANLS)")
+        plt.title("Hallucination by image (interlocked methods)")
+        plt.grid(axis='y', linestyle='--', alpha=0.3)
+        # Add box with per-method mean
+        text2 = "\n".join([f"{m}: {float(combo_df[combo_df['method']==m]['hallucination_score'].mean()):.3f}" for m in methods])
+        plt.gca().text(0.99, 0.01, text2, transform=plt.gca().transAxes,
+                       fontsize=9, va='bottom', ha='right',
+                       bbox=dict(boxstyle='round', facecolor='white', alpha=0.8, edgecolor='gray'))
+        plt.legend(loc='upper right', ncol=min(3, len(methods)))
+        plt.tight_layout()
+        group_line_png = combined_dir / "hallucination_interlocked_by_image.png"
+        plt.savefig(group_line_png, dpi=160)
+        plt.close()
+
+        print(f"Combined CSV: {combined_csv}")
+        print(f"Saved: {bar_png}")
+        print(f"Saved: {heatmap_png}")
+        print(f"Saved: {line_png}")
+        print(f"Saved: {group_line_png}")
+
+    if any_error:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
+
+
--- a/scripts/pipeline_compare.py
+++ b/scripts/pipeline_compare.py
@@ -0,0 +1,254 @@
+#!/usr/bin/env python3
+"""
+Compare hallucination across three pipelines over five preprocessing methods:
+  1) Raw: all images
+  2) DeQA-filtered: keep images with DeQA score >= threshold (default 2.6)
+  3) Human-filtered: keep images labeled High in CSV labels
+
+Inputs:
+  - One or more per_sample_eval.json files (or per_image_anls.csv already generated)
+  - DeQA score file (txt): lines like "3.9  - image (9)_0.png"
+  - Human labels CSV with columns: filename,label where label in {High,Low}
+
+Outputs:
+  - Combined means CSV: method vs mean hallucination for each pipeline
+  - Line chart (3 lines): hallucination mean per method across the three pipelines
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import re
+from pathlib import Path
+from typing import Dict, List, Tuple, Optional
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def canonical_key(name: str) -> str:
+    """Map various filenames to a canonical key used by per_sample_eval 'image' field.
+
+    Examples:
+      - "image (9)_0.png" -> "image (9)"
+      - "image (22)" -> "image (22)"
+      - "foo/bar/image (15)_3.jpg" -> "image (15)"
+      - other names -> stem without extension
+    """
+    if not name:
+        return name
+    # Keep only basename
+    base = Path(name).name
+    # Try pattern image (N)
+    m = re.search(r"(image \(\d+\))", base, flags=re.IGNORECASE)
+    if m:
+        return m.group(1)
+    # Fallback: remove extension
+    return Path(base).stem
+
+
+def read_deqa_scores(txt_path: Path) -> Dict[str, float]:
+    scores: Dict[str, float] = {}
+    with txt_path.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            # Accept formats: "3.9  - filename" or "filename,3.9" etc.
+            m = re.match(r"\s*([0-9]+(?:\.[0-9]+)?)\s*[-,:]?\s*(.+)$", line)
+            if m:
+                score = float(m.group(1))
+                filename = m.group(2)
+            else:
+                parts = re.split(r"[,\t]", line)
+                if len(parts) >= 2:
+                    try:
+                        score = float(parts[1])
+                        filename = parts[0]
+                    except Exception:
+                        continue
+                else:
+                    continue
+            key = canonical_key(filename)
+            scores[key] = score
+    return scores
+
+
+def read_human_labels(csv_path: Path) -> Dict[str, str]:
+    labels: Dict[str, str] = {}
+    with csv_path.open("r", encoding="utf-8") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            filename = (row.get("filename") or row.get("file") or "").strip()
+            label = (row.get("label") or row.get("Label") or "").strip()
+            if not filename:
+                continue
+            key = canonical_key(filename)
+            if label:
+                labels[key] = label
+    return labels
+
+
+def levenshtein_distance(a: str, b: str) -> int:
+    if a == b:
+        return 0
+    if len(a) == 0:
+        return len(b)
+    if len(b) == 0:
+        return len(a)
+    previous_row = list(range(len(b) + 1))
+    for i, ca in enumerate(a, start=1):
+        current_row = [i]
+        for j, cb in enumerate(b, start=1):
+            insertions = previous_row[j] + 1
+            deletions = current_row[j - 1] + 1
+            substitutions = previous_row[j - 1] + (0 if ca == cb else 1)
+            current_row.append(min(insertions, deletions, substitutions))
+        previous_row = current_row
+    return previous_row[-1]
+
+
+def normalized_similarity(pred: str, gt: str) -> float:
+    pred = pred or ""
+    gt = gt or ""
+    max_len = max(len(pred), len(gt))
+    if max_len == 0:
+        return 1.0
+    dist = levenshtein_distance(pred, gt)
+    sim = 1.0 - (dist / max_len)
+    if sim < 0.0:
+        return 0.0
+    if sim > 1.0:
+        return 1.0
+    return sim
+
+
+def compute_anls_for_record(record: Dict) -> tuple[float, int]:
+    fields = record.get("fields") or []
+    if not isinstance(fields, list) or len(fields) == 0:
+        return 0.0, 0
+    sims: List[float] = []
+    for f in fields:
+        pred = str(f.get("pred", ""))
+        gt = str(f.get("gt", ""))
+        sims.append(normalized_similarity(pred, gt))
+    anls = float(sum(sims) / len(sims)) if sims else 0.0
+    return anls, len(sims)
+
+
+def load_per_image_anls(input_json: Path) -> pd.DataFrame:
+    # Prefer existing per_image_anls.csv, otherwise compute quickly
+    per_image_csv = input_json.parent / "per_image_anls.csv"
+    if per_image_csv.exists():
+        df = pd.read_csv(per_image_csv)
+        return df
+    # Fallback: compute minimal ANLS like in the other script
+    with input_json.open("r", encoding="utf-8") as f:
+        data = json.load(f)
+    rows = []
+    for rec in data:
+        anls, num_fields = compute_anls_for_record(rec)
+        rows.append({
+            "image": rec.get("image"),
+            "anls": anls,
+            "hallucination_score": 1.0 - anls,
+            "num_fields": int(num_fields),
+        })
+    return pd.DataFrame(rows)
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description="Compare hallucination across raw/DeQA/Human pipelines over methods")
+    p.add_argument("inputs", nargs="+", help="per_sample_eval.json files for each method")
+    p.add_argument("--deqa_txt", required=True, help="Path to DeQA scores txt (e.g., cni.txt)")
+    p.add_argument("--human_csv", required=True, help="Path to human labels CSV")
+    p.add_argument("--deqa_threshold", type=float, default=2.6, help="DeQA threshold (>=)")
+    args = p.parse_args()
+
+    # Load filters
+    deqa_scores = read_deqa_scores(Path(args.deqa_txt))
+    human_labels = read_human_labels(Path(args.human_csv))
+
+    # Aggregate per method
+    method_to_df: Dict[str, pd.DataFrame] = {}
+    for ip in args.inputs:
+        path = Path(ip)
+        df = load_per_image_anls(path)
+        df["method"] = path.parent.name
+        df["image_key"] = df["image"].apply(canonical_key)
+        method_to_df[path.parent.name] = df
+
+    # Compute means per pipeline (fair comparison: set excluded images to hallucination=0)
+    records = []
+    for method, df in method_to_df.items():
+        raw_mean = float(df["hallucination_score"].mean()) if len(df) else float("nan")
+
+        # DeQA filter: mark DeQA < threshold as hallucination=0, keep all images
+        df_deqa = df.copy()
+        mask_deqa = df_deqa["image_key"].map(lambda k: deqa_scores.get(k, None))
+        # Set hallucination=0 for images with DeQA < threshold (or missing DeQA)
+        df_deqa.loc[mask_deqa.isna() | (mask_deqa < args.deqa_threshold), "hallucination_score"] = 0.0
+        deqa_mean = float(df_deqa["hallucination_score"].mean()) if len(df_deqa) else float("nan")
+
+        # Human filter: mark Low labels as hallucination=0, keep all images
+        df_human = df.copy()
+        mask_human = df_human["image_key"].map(lambda k: human_labels.get(k, "").lower())
+        # Set hallucination=0 for images labeled Low (or missing label)
+        df_human.loc[mask_human != "high", "hallucination_score"] = 0.0
+        human_mean = float(df_human["hallucination_score"].mean()) if len(df_human) else float("nan")
+
+        records.append({
+            "method": method,
+            "raw_mean": raw_mean,
+            "deqa_mean": deqa_mean,
+            "human_mean": human_mean,
+            "raw_count": int(len(df)),
+            "deqa_count": int(len(df_deqa)),  # Now equal to raw_count
+            "human_count": int(len(df_human)),  # Now equal to raw_count
+        })
+
+    outdir = Path(args.inputs[0]).parent.parent / "combined_anls" / "pipeline"
+    outdir.mkdir(parents=True, exist_ok=True)
+    out_csv = outdir / "pipeline_means.csv"
+    means_df = pd.DataFrame(records).sort_values("method")
+    means_df.to_csv(out_csv, index=False)
+
+    # 3-line comparison plot over methods (narrower with score annotations)
+    x = range(len(means_df))
+    plt.figure(figsize=(7, 5))
+    
+    # Plot lines and add score annotations
+    raw_vals = means_df["raw_mean"].values
+    deqa_vals = means_df["deqa_mean"].values
+    human_vals = means_df["human_mean"].values
+    
+    plt.plot(x, raw_vals, marker="o", label="Raw", linewidth=2, markersize=6)
+    plt.plot(x, deqa_vals, marker="s", label=f"DeQA >= {args.deqa_threshold}", linewidth=2, markersize=6)
+    plt.plot(x, human_vals, marker="^", label="Human High", linewidth=2, markersize=6)
+    
+    # Annotate each point with its score
+    for i, (r, d, h) in enumerate(zip(raw_vals, deqa_vals, human_vals)):
+        plt.annotate(f"{r:.3f}", (i, r), textcoords="offset points", xytext=(0,8), ha='center', fontsize=8)
+        plt.annotate(f"{d:.3f}", (i, d), textcoords="offset points", xytext=(0,8), ha='center', fontsize=8)
+        plt.annotate(f"{h:.3f}", (i, h), textcoords="offset points", xytext=(0,8), ha='center', fontsize=8)
+    
+    plt.xticks(list(x), means_df["method"].tolist(), rotation=25, ha="right")
+    plt.ylabel("Mean hallucination (1 - ANLS)")
+    plt.title("Pipeline comparison over preprocessing methods")
+    plt.grid(axis="y", linestyle="--", alpha=0.3)
+    plt.legend()
+    plt.tight_layout()
+    out_png = outdir / "pipeline_comparison.png"
+    plt.savefig(out_png, dpi=160)
+    plt.close()
+
+    print(f"Saved: {out_csv}")
+    print(f"Saved: {out_png}")
+
+
+if __name__ == "__main__":
+    main()
+
+