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@ -12,7 +12,7 @@
![](https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg) ![](https://user-images.githubusercontent.com/952378/102350683-8a74db80-3fa5-11eb-8c7e-f743f7d6eae2.jpg)
## Features ## Features
* Document layout analysis using pixelwise segmentation models with support for 10 distinct segmentation classes: * Document layout analysis using pixelwise segmentation models with support for 10 segmentation classes:
* background, [page border](https://ocr-d.de/en/gt-guidelines/trans/lyRand.html), [text region](https://ocr-d.de/en/gt-guidelines/trans/lytextregion.html#textregionen__textregion_), [text line](https://ocr-d.de/en/gt-guidelines/pagexml/pagecontent_xsd_Complex_Type_pc_TextLineType.html), [header](https://ocr-d.de/en/gt-guidelines/trans/lyUeberschrift.html), [image](https://ocr-d.de/en/gt-guidelines/trans/lyBildbereiche.html), [separator](https://ocr-d.de/en/gt-guidelines/trans/lySeparatoren.html), [marginalia](https://ocr-d.de/en/gt-guidelines/trans/lyMarginalie.html), [initial](https://ocr-d.de/en/gt-guidelines/trans/lyInitiale.html), [table](https://ocr-d.de/en/gt-guidelines/trans/lyTabellen.html) * background, [page border](https://ocr-d.de/en/gt-guidelines/trans/lyRand.html), [text region](https://ocr-d.de/en/gt-guidelines/trans/lytextregion.html#textregionen__textregion_), [text line](https://ocr-d.de/en/gt-guidelines/pagexml/pagecontent_xsd_Complex_Type_pc_TextLineType.html), [header](https://ocr-d.de/en/gt-guidelines/trans/lyUeberschrift.html), [image](https://ocr-d.de/en/gt-guidelines/trans/lyBildbereiche.html), [separator](https://ocr-d.de/en/gt-guidelines/trans/lySeparatoren.html), [marginalia](https://ocr-d.de/en/gt-guidelines/trans/lyMarginalie.html), [initial](https://ocr-d.de/en/gt-guidelines/trans/lyInitiale.html), [table](https://ocr-d.de/en/gt-guidelines/trans/lyTabellen.html)
* Textline segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text * Textline segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text
* Document image binarization with pixelwise segmentation or hybrid CNN-Transformer models * Document image binarization with pixelwise segmentation or hybrid CNN-Transformer models
@ -81,6 +81,8 @@ Eynollah supports five use cases:
4. [text recognition (OCR)](#ocr), and 4. [text recognition (OCR)](#ocr), and
5. [reading order detection](#reading-order-detection). 5. [reading order detection](#reading-order-detection).
Some example outputs can be found in [`examples.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/examples.md).
### Layout Analysis ### Layout Analysis
The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading
@ -152,16 +154,6 @@ TODO
### OCR ### OCR
<p align="center">
<img src="https://github.com/user-attachments/assets/71054636-51c6-4117-b3cf-361c5cda3528" alt="Input Image" width="45%">
<img src="https://github.com/user-attachments/assets/cfb3ce38-007a-4037-b547-21324a7d56dd" alt="Output Image" width="45%">
</p>
<p align="center">
<img src="https://github.com/user-attachments/assets/343b2ed8-d818-4d4a-b301-f304cbbebfcd" alt="Input Image" width="45%">
<img src="https://github.com/user-attachments/assets/accb5ba7-e37f-477e-84aa-92eafa0d136e" alt="Output Image" width="45%">
</p>
The OCR module performs text recognition using either a CNN-RNN model or a Transformer model. The OCR module performs text recognition using either a CNN-RNN model or a Transformer model.
The command-line interface for OCR can be called like this: The command-line interface for OCR can be called like this:
@ -176,17 +168,17 @@ eynollah ocr \
The following options can be used to further configure the ocr processing: The following options can be used to further configure the ocr processing:
| option | description | | option | description |
|-------------------|:------------------------------------------------------------------------------- | |-------------------|:-------------------------------------------------------------------------------------------|
| `-dib` | directory of bins(files type must be '.png'). Prediction with both RGB and bins. | | `-dib` | directory of binarized images (file type must be '.png'), prediction with both RGB and bin |
| `-doit` | Directory containing output images rendered with the predicted text | | `-doit` | directory for output images rendered with the predicted text |
| `--model_name` | Specific model file path to use for OCR | | `--model_name` | file path to use specific model for OCR |
| `-trocr` | transformer ocr will be applied, otherwise cnn_rnn model | | `-trocr` | use transformer ocr model (otherwise cnn_rnn model is used) |
| `-etit` | textlines images and text in xml will be exported into output dir (OCR training data) | | `-etit` | export textline images and text in xml to output dir (OCR training data) |
| `-nmtc` | cropped textline images will not be masked with textline contour | | `-nmtc` | cropped textline images will not be masked with textline contour |
| `-bs` | ocr inference batch size. Default bs for trocr and cnn_rnn models are 2 and 8 respectively | | `-bs` | ocr inference batch size. Default batch size is 2 for trocr and 8 for cnn_rnn models |
| `-ds_pref` | add an abbrevation of dataset name to generated training data | | `-ds_pref` | add an abbrevation of dataset name to generated training data |
| `-min_conf` | minimum OCR confidence value. OCRs with textline conf lower than this will be ignored | | `-min_conf` | minimum OCR confidence value. OCR with textline conf lower than this will be ignored |
### Reading Order Detection ### Reading Order Detection