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Merge remote-tracking branch 'origin/updating_docs' into docs_and_minor_fixes
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README.md
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README.md
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@ -11,6 +11,11 @@
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<p align="center">
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<img src="https://github.com/user-attachments/assets/42df2582-4579-415e-92f1-54858a02c830" alt="Input Image" width="45%">
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<img src="https://github.com/user-attachments/assets/77fc819e-6302-4fc9-967c-ee11d10d863e" alt="Output Image" width="45%">
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</p>
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## Features
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* Document layout analysis using pixelwise segmentation models with support for 10 distinct segmentation classes:
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* 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)
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@ -98,24 +103,35 @@ eynollah layout \
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The following options can be used to further configure the processing:
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| option | description |
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|-------------------|:-------------------------------------------------------------------------------|
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| `-fl` | full layout analysis including all steps and segmentation classes |
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| `-light` | lighter and faster but simpler method for main region detection and deskewing |
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| `-tll` | this indicates the light textline and should be passed with light version |
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| `-tab` | apply table detection |
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| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
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| `-as` | apply scaling |
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| `-cl` | apply contour detection for curved text lines instead of bounding boxes |
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| `-ib` | apply binarization (the resulting image is saved to the output directory) |
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| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
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| `-eoi` | extract only images to output directory (other processing will not be done) |
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| `-ho` | ignore headers for reading order dectection |
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| `-si <directory>` | save image regions detected to this directory |
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| `-sd <directory>` | save deskewed image to this directory |
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| `-sl <directory>` | save layout prediction as plot to this directory |
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| `-sp <directory>` | save cropped page image to this directory |
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| `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory |
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| option | description |
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|-------------------|:------------------------------------------------------------------------------- |
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| `-fl` | full layout analysis including all steps and segmentation classes (recommended) |
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| `-light` | lighter and faster but simpler method for main region detection and deskewing (recommended) |
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| `-tll` | this indicates the light textline and should be passed with light version (recommended) |
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| `-tab` | apply table detection |
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| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
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| `-as` | apply scaling |
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| `-cl` | apply contour detection for curved text lines instead of bounding boxes |
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| `-ib` | apply binarization (the resulting image is saved to the output directory) |
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| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
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| `-eoi` | extract only images to output directory (other processing will not be done) |
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| `-ho` | ignore headers for reading order dectection |
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| `-si <directory>` | save image regions detected to this directory |
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| `-sd <directory>` | save deskewed image to this directory |
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| `-sl <directory>` | save layout prediction as plot to this directory |
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| `-sp <directory>` | save cropped page image to this directory |
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| `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory |
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| `-thart` | threshold of artifical class in the case of textline detection. The default value is 0.1 |
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| `-tharl` | threshold of artifical class in the case of layout detection. The default value is 0.1 |
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| `-ocr` | do ocr |
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| `-tr` | apply transformer ocr. Default model is a CNN-RNN model |
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| `-bs_ocr` | ocr inference batch size. Default bs for trocr and cnn_rnn models are 2 and 8 respectively |
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| `-ncu` | upper limit of columns in document image |
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| `-ncl` | lower limit of columns in document image |
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| `-slro` | skip layout detection and reading order |
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| `-romb` | apply machine based reading order detection |
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| `-ipe` | ignore page extraction |
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If no further option is set, the tool performs layout detection of main regions (background, text, images, separators
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and marginals).
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@ -133,7 +149,7 @@ The command-line interface for binarization can be called like this:
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eynollah binarization \
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-i <single image file> | -di <directory containing image files> \
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-o <output directory> \
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-m <directory containing model files> \
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-m <directory containing model files>
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```
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### Image Enhancement
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@ -141,7 +157,17 @@ TODO
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### OCR
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The OCR module performs text recognition using either CNN-RNN or TrOCR models.
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<p align="center">
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<img src="https://github.com/user-attachments/assets/71054636-51c6-4117-b3cf-361c5cda3528" alt="Input Image" width="45%">
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<img src="https://github.com/user-attachments/assets/cfb3ce38-007a-4037-b547-21324a7d56dd" alt="Output Image" width="45%">
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</p>
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<p align="center">
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<img src="https://github.com/user-attachments/assets/343b2ed8-d818-4d4a-b301-f304cbbebfcd" alt="Input Image" width="45%">
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<img src="https://github.com/user-attachments/assets/accb5ba7-e37f-477e-84aa-92eafa0d136e" alt="Output Image" width="45%">
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</p>
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The OCR module performs text recognition using either a CNN-RNN model or a Transformer model.
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The command-line interface for OCR can be called like this:
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@ -150,9 +176,24 @@ eynollah ocr \
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-i <single image file> | -di <directory containing image files> \
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-dx <directory of xmls> \
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-o <output directory> \
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-m <directory containing model files> | --model_name <path to specific model> \
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-m <directory containing model files> | --model_name <path to specific model>
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```
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The following options can be used to further configure the ocr processing:
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| option | description |
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|-------------------|:------------------------------------------------------------------------------- |
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| `-dib` | directory of bins(files type must be '.png'). Prediction with both RGB and bins. |
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| `-doit` | Directory containing output images rendered with the predicted text |
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| `--model_name` | Specific model file path to use for OCR |
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| `-trocr` | transformer ocr will be applied, otherwise cnn_rnn model |
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| `-etit` | textlines images and text in xml will be exported into output dir (OCR training data) |
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| `-nmtc` | cropped textline images will not be masked with textline contour |
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| `-bs` | ocr inference batch size. Default bs for trocr and cnn_rnn models are 2 and 8 respectively |
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| `-ds_pref` | add an abbrevation of dataset name to generated training data |
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| `-min_conf` | minimum OCR confidence value. OCRs with textline conf lower than this will be ignored |
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### Reading Order Detection
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Reading order detection can be performed either as part of layout analysis based on image input, or, currently under
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development, based on pre-existing layout analysis data in PAGE-XML format as input.
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