Merge pull request #3835 from myhloli/dev

chore: update changelog for 2.6.2 release with OCR model optimizations and backend improvements

README.md

@@ -44,11 +44,11 @@
 </div>
 
 # Changelog
-- 2025/10/24 2.6.1 Release
+- 2025/10/24 2.6.2 Release
   - `pipeline` backend optimizations
     - Added experimental support for Chinese formulas, which can be enabled by setting the environment variable `export MINERU_FORMULA_CH_SUPPORT=1`. This feature may cause a slight decrease in MFR speed and failures in recognizing some long formulas. It is recommended to enable it only when parsing Chinese formulas is needed. To disable this feature, set the environment variable to `0`.
     - `OCR` speed significantly improved by 200%~300%, thanks to the optimization solution provided by [@cjsdurj](https://github.com/cjsdurj)
-    - `OCR` models updated to `ppocr-v5` version for Cyrillic, Arabic, Devanagari, Telugu (te), and Tamil (ta) languages, with accuracy improved by over 40% compared to previous models
+    - `OCR` models optimized for improved accuracy and coverage of Latin script recognition, and updated Cyrillic, Arabic, Devanagari, Telugu (te), and Tamil (ta) language systems to `ppocr-v5` version, with accuracy improved by over 40% compared to previous models 
   - `vlm` backend optimizations
     - `table_caption` and `table_footnote` matching logic optimized to improve the accuracy of table caption and footnote matching and reading order rationality in scenarios with multiple consecutive tables on a page
     - Optimized CPU resource usage during high concurrency when using `vllm` backend, reducing server pressure

README_zh-CN.md

@@ -44,11 +44,11 @@
 </div>
 
 # 更新记录
-- 2025/10/24 2.6.1 发布
+- 2025/10/24 2.6.2 发布
   - `pipline`后端优化
     - 增加对中文公式的实验性支持，可通过配置环境变量`export MINERU_FORMULA_CH_SUPPORT=1`开启。该功能可能会导致MFR速率略微下降、部分长公式识别失败等问题，建议仅在需要解析中文公式的场景下开启。如需关闭该功能，可将环境变量设置为`0`。
     - `OCR`速度大幅提升200%~300%，感谢 [@cjsdurj](https://github.com/cjsdurj) 提供的优化方案
-    - `OCR`模型更新西里尔文(cyrillic)、阿拉伯文(arabic)、天城文(devanagari)、泰卢固语(te)、泰米尔语(ta)语系至`ppocr-v5`版本，精度相比上代模型提升40%以上
+    - `OCR`模型优化拉丁文识别的准度和广度，并更新西里尔文(cyrillic)、阿拉伯文(arabic)、天城文(devanagari)、泰卢固语(te)、泰米尔语(ta)语系至`ppocr-v5`版本，精度相比上代模型提升40%以上
   - `vlm`后端优化
     - `table_caption`、`table_footnote`匹配逻辑优化，提升页内多张连续表场景下的表格标题和脚注的匹配准确率和阅读顺序合理性
     - 优化使用`vllm`后端时高并发时的cpu资源占用，降低服务端压力

mineru/model/utils/pytorchocr/modeling/backbones/rec_lcnetv3.py

@@ -256,7 +256,7 @@ class LearnableRepLayer(nn.Module):
                 input_dim = self.in_channels // self.groups
                 kernel_value = torch.zeros(
                     (self.in_channels, input_dim, self.kernel_size, self.kernel_size),
-                    dtype=branch.weight.dtype,  device=branch.weight.device,
+                    dtype=branch.weight.dtype, device=branch.weight.device,
                 )
                 for i in range(self.in_channels):
                     kernel_value[

mineru/model/utils/pytorchocr/utils/resources/arch_config.yaml

@@ -28,82 +28,6 @@ Multilingual_PP-OCRv3_det_infer:
     name: DBHead
     k: 50
 
-en_PP-OCRv3_det_infer:
-  model_type: det
-  algorithm: DB
-  Transform:
-  Backbone:
-    name: MobileNetV3
-    scale: 0.5
-    model_name: large
-    disable_se: True
-  Neck:
-    name: RSEFPN
-    out_channels: 96
-    shortcut: True
-  Head:
-    name: DBHead
-    k: 50
-
-ch_PP-OCRv3_det_infer:
-  model_type: det
-  algorithm: DB
-  Transform:
-  Backbone:
-    name: MobileNetV3
-    scale: 0.5
-    model_name: large
-    disable_se: True
-  Neck:
-    name: RSEFPN
-    out_channels: 96
-    shortcut: True
-  Head:
-    name: DBHead
-    k: 50
-
-en_PP-OCRv4_rec_infer:
-  model_type: rec
-  algorithm: SVTR_LCNet
-  Transform:
-  Backbone:
-    name: PPLCNetV3
-    scale: 0.95
-  Head:
-    name: MultiHead
-    out_channels_list:
-      CTCLabelDecode: 97 #'blank' + ...(62) + ' '
-    head_list:
-      - CTCHead:
-          Neck:
-            name: svtr
-            dims: 120
-            depth: 2
-            hidden_dims: 120
-            kernel_size: [ 1, 3 ]
-            use_guide: True
-          Head:
-            fc_decay: 0.00001
-      - NRTRHead:
-          nrtr_dim: 384
-          max_text_length: 25
-
-ch_PP-OCRv4_det_infer:
-  model_type: det
-  algorithm: DB
-  Transform: null
-  Backbone:
-    name: PPLCNetV3
-    scale: 0.75
-    det: True
-  Neck:
-    name: RSEFPN
-    out_channels: 96
-    shortcut: True
-  Head:
-    name: DBHead
-    k: 50
-
 ch_PP-OCRv5_det_infer:
   model_type: det
   algorithm: DB
@@ -152,57 +76,6 @@ ch_PP-OCRv4_det_server_infer:
     k: 50
     mode: "large"
 
-ch_PP-OCRv4_rec_infer:
-  model_type: rec
-  algorithm: SVTR_LCNet
-  Transform:
-  Backbone:
-    name: PPLCNetV3
-    scale: 0.95
-  Head:
-    name: MultiHead
-    out_channels_list:
-      CTCLabelDecode: 6625 #'blank' + ...(6623) + ' '
-    head_list:
-      - CTCHead:
-          Neck:
-            name: svtr
-            dims: 120
-            depth: 2
-            hidden_dims: 120
-            kernel_size: [ 1, 3 ]
-            use_guide: True
-          Head:
-            fc_decay: 0.00001
-      - NRTRHead:
-          nrtr_dim: 384
-          max_text_length: 25
-
-ch_PP-OCRv4_rec_server_infer:
-  model_type: rec
-  algorithm: SVTR_HGNet
-  Transform:
-  Backbone:
-    name: PPHGNet_small
-  Head:
-    name: MultiHead
-    out_channels_list:
-      CTCLabelDecode: 6625 #'blank' + ...(6623) + ' '
-    head_list:
-      - CTCHead:
-          Neck:
-            name: svtr
-            dims: 120
-            depth: 2
-            hidden_dims: 120
-            kernel_size: [ 1, 3 ]
-            use_guide: True
-          Head:
-            fc_decay: 0.00001
-      - NRTRHead:
-          nrtr_dim: 384
-          max_text_length: 25
-
 ch_PP-OCRv4_rec_server_doc_infer:
   model_type: rec
   algorithm: SVTR_HGNet
@@ -280,174 +153,6 @@ ch_PP-OCRv5_rec_infer:
           nrtr_dim: 384
           max_text_length: 25
 
-chinese_cht_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [1, 2]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 8423
-    fc_decay: 0.00001
-
-latin_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 187
-    fc_decay: 0.00001
-
-cyrillic_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 165
-    fc_decay: 0.00001
-
-arabic_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 164
-    fc_decay: 0.00001
-
-korean_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 3690
-    fc_decay: 0.00001
-
-japan_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 4401
-    fc_decay: 0.00001
-
-ta_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 130
-    fc_decay: 0.00001
-
-te_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 153
-    fc_decay: 0.00001
-
 ka_PP-OCRv3_rec_infer:
   model_type: rec
   algorithm: SVTR
@@ -469,27 +174,6 @@ ka_PP-OCRv3_rec_infer:
 #    out_channels: 155
     fc_decay: 0.00001
 
-devanagari_PP-OCRv3_rec_infer:
-  model_type: rec
-  algorithm: SVTR
-  Transform:
-  Backbone:
-    name: MobileNetV1Enhance
-    scale: 0.5
-    last_conv_stride: [ 1, 2 ]
-    last_pool_type: avg
-  Neck:
-    name: SequenceEncoder
-    encoder_type: svtr
-    dims: 64
-    depth: 2
-    hidden_dims: 120
-    use_guide: True
-  Head:
-    name: CTCHead
-#    out_channels: 169
-    fc_decay: 0.00001
-
 korean_PP-OCRv5_rec_infer:
   model_type: rec
   algorithm: SVTR_HGNet
@@ -526,7 +210,7 @@ latin_PP-OCRv5_rec_infer:
   Head:
     name: MultiHead
     out_channels_list:
-      CTCLabelDecode: 504
+      CTCLabelDecode: 838
     head_list:
       - CTCHead:
           Neck:

mineru/model/utils/pytorchocr/utils/resources/dict/arabic_dict.txt

@@ -1,162 +0,0 @@
- 
-!
-#
-$
-%
-&
-'
-(
-+
-,
--
-.
-/
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-:
-?
-@
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
-_
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z
-É
-é
-ء
-آ
-أ
-ؤ
-إ
-ئ
-ا
-ب
-ة
-ت
-ث
-ج
-ح
-خ
-د
-ذ
-ر
-ز
-س
-ش
-ص
-ض
-ط
-ظ
-ع
-غ
-ف
-ق
-ك
-ل
-م
-ن
-ه
-و
-ى
-ي
-ً
-ٌ
-ٍ
-َ
-ُ
-ِ
-ّ
-ْ
-ٓ
-ٔ
-ٰ
-ٱ
-ٹ
-پ
-چ
-ڈ
-ڑ
-ژ
-ک
-ڭ
-گ
-ں
-ھ
-ۀ
-ہ
-ۂ
-ۃ
-ۆ
-ۇ
-ۈ
-ۋ
-ی
-ې
-ے
-ۓ
-ە
-١
-٢
-٣
-٤
-٥
-٦
-٧
-٨
-٩

mineru/model/utils/pytorchocr/utils/resources/dict/cyrillic_dict.txt

@@ -1,163 +0,0 @@
- 
-!
-#
-$
-%
-&
-'
-(
-+
-,
--
-.
-/
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-:
-?
-@
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
-_
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z
-É
-é
-Ё
-Є
-І
-Ј
-Љ
-Ў
-А
-Б
-В
-Г
-Д
-Е
-Ж
-З
-И
-Й
-К
-Л
-М
-Н
-О
-П
-Р
-С
-Т
-У
-Ф
-Х
-Ц
-Ч
-Ш
-Щ
-Ъ
-Ы
-Ь
-Э
-Ю
-Я
-а
-б
-в
-г
-д
-е
-ж
-з
-и
-й
-к
-л
-м
-н
-о
-п
-р
-с
-т
-у
-ф
-х
-ц
-ч
-ш
-щ
-ъ
-ы
-ь
-э
-ю
-я
-ё
-ђ
-є
-і
-ј
-љ
-њ
-ћ
-ў
-џ
-Ґ
-ґ

mineru/model/utils/pytorchocr/utils/resources/dict/devanagari_dict.txt

@@ -1,167 +0,0 @@
- 
-!
-#
-$
-%
-&
-'
-(
-+
-,
--
-.
-/
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-:
-?
-@
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
-_
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z
-É
-é
-ँ
-ं
-ः
-अ
-आ
-इ
-ई
-उ
-ऊ
-ऋ
-ए
-ऐ
-ऑ
-ओ
-औ
-क
-ख
-ग
-घ
-ङ
-च
-छ
-ज
-झ
-ञ
-ट
-ठ
-ड
-ढ
-ण
-त
-थ
-द
-ध
-न
-ऩ
-प
-फ
-ब
-भ
-म
-य
-र
-ऱ
-ल
-ळ
-व
-श
-ष
-स
-ह
-़
-ा
-ि
-ी
-ु
-ू
-ृ
-ॅ
-े
-ै
-ॉ
-ो
-ौ
-्
-॒
-क़
-ख़
-ग़
-ज़
-ड़
-ढ़
-फ़
-ॠ
-।
-०
-१
-२
-३
-४
-५
-६
-७
-८
-९
-॰

mineru/model/utils/pytorchocr/utils/resources/dict/en_dict.txt

@@ -1,95 +0,0 @@
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-:
-;
-<
-=
->
-?
-@
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
-[
-\
-]
-^
-_
-`
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z
-{
-|
-}
-~
-!
-"
-#
-$
-%
-&
-'
-(
-)
-*
-+
-,
--
-.
-/
- 

mineru/model/utils/pytorchocr/utils/resources/dict/latin_dict.txt

@@ -1,185 +0,0 @@
- 
-!
-"
-#
-$
-%
-&
-'
-(
-)
-*
-+
-,
--
-.
-/
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-:
-;
-<
-=
->
-?
-@
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
-[
-]
-_
-`
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z
-{
-}
-¡
-£
-§
-ª
-«
-­
-°
-²
-³
-´
-µ
-·
-º
-»
-¿
-À
-Á
-Â
-Ä
-Å
-Ç
-È
-É
-Ê
-Ë
-Ì
-Í
-Î
-Ï
-Ò
-Ó
-Ô
-Õ
-Ö
-Ú
-Ü
-Ý
-ß
-à
-á
-â
-ã
-ä
-å
-æ
-ç
-è
-é
-ê
-ë
-ì
-í
-î
-ï
-ñ
-ò
-ó
-ô
-õ
-ö
-ø
-ù
-ú
-û
-ü
-ý
-ą
-Ć
-ć
-Č
-č
-Đ
-đ
-ę
-ı
-Ł
-ł
-ō
-Œ
-œ
-Š
-š
-Ÿ
-Ž
-ž
-ʒ
-β
-δ
-ε
-з
-Ṡ
-‘
-€
-™

mineru/model/utils/pytorchocr/utils/resources/dict/ppocrv5_latin_dict.txt

@@ -1,18 +1,3 @@
-!
-"
-#
-$
-%
-&
-'
-(
-)
-*
-+
-,
--
-.
-/
 0
 1
 2
@@ -23,13 +8,6 @@ $
 7
 8
 9
-:
-;
-<
-=
->
-?
-@
 A
 B
 C
@@ -56,12 +34,6 @@ W
 X
 Y
 Z
-[
-\
-]
-^
-_
-`
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mineru/model/utils/pytorchocr/utils/resources/dict/ta_dict.txt

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mineru/model/utils/pytorchocr/utils/resources/models_config.yml

@@ -3,26 +3,14 @@ lang:
     det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv5_rec_infer.pth
     dict: ppocrv5_dict.txt
-  ch_lite_v4:
-    det: ch_PP-OCRv5_det_infer.pth
-    rec: ch_PP-OCRv4_rec_infer.pth
-    dict: ppocr_keys_v1.txt
   ch_server:
     det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv5_rec_server_infer.pth
     dict: ppocrv5_dict.txt
-  ch_server_v4:
-    det: ch_PP-OCRv5_det_infer.pth
-    rec: ch_PP-OCRv4_rec_server_infer.pth
-    dict: ppocr_keys_v1.txt
   ch:
     det: ch_PP-OCRv5_det_infer.pth
     rec: ch_PP-OCRv4_rec_server_doc_infer.pth
     dict: ppocrv4_doc_dict.txt
-  en_v4:
-    det: en_PP-OCRv3_det_infer.pth
-    rec: en_PP-OCRv4_rec_infer.pth
-    dict: en_dict.txt
   korean:
     det: ch_PP-OCRv5_det_infer.pth
     rec: korean_PP-OCRv5_rec_infer.pth

mineru/model/utils/tools/infer/predict_rec.py

@@ -137,7 +137,7 @@ class TextRecognizer(BaseOCRV20):
         h, w = img.shape[:2]
         ratio = w / float(h)
         ratio_imgH = max(math.ceil(imgH * ratio), self.limited_min_width)
-        resized_w = min(imgW,int(ratio_imgH))
+        resized_w = min(imgW, int(ratio_imgH))
         resized_image = cv2.resize(img, (resized_w, imgH)) /127.5 - 1
         padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
         padding_im[:, :, 0:resized_w] = resized_image.transpose((2, 0, 1))

mineru/version.py

@@ -1 +1 @@
-__version__ = "2.5.4"
+__version__ = "2.6.1"