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@ -92,10 +92,10 @@ eynollah layout \
The following options can be used to further configure the processing:
| option | description |
|-------------------|:-------------------------------------------------------------------------------|
| `-fl` | full layout analysis including all steps and segmentation classes |
| `-light` | lighter and faster but simpler method for main region detection and deskewing |
| `-tll` | this indicates the light textline and should be passed with light version |
|-------------------|:------------------------------------------------------------------------------- |
| `-fl` | full layout analysis including all steps and segmentation classes (recommended) |
| `-light` | lighter and faster but simpler method for main region detection and deskewing (recommended) |
| `-tll` | this indicates the light textline and should be passed with light version (recommended) |
| `-tab` | apply table detection |
| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
| `-as` | apply scaling |
@ -109,6 +109,17 @@ The following options can be used to further configure the processing:
| `-sl <directory>` | save layout prediction as plot to this directory |
| `-sp <directory>` | save cropped page image to this directory |
| `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory |
| `-thart` | threshold of artifical class in the case of textline detection. The default value is 0.1 |
| `-tharl` | threshold of artifical class in the case of layout detection. The default value is 0.1 |
| `-ocr` | do ocr |
| `-tr` | apply transformer ocr. Default model is a CNN-RNN model |
| `-bs_ocr` | ocr inference batch size. Default bs for trocr and cnn_rnn models are 2 and 8 respectively |
| `-ncu` | upper limit of columns in document image |
| `-ncl` | lower limit of columns in document image |
| `-slro` | skip layout detection and reading order |
| `-romb` | apply machine based reading order detection |
| `-ipe` | ignore page extraction |
If no further option is set, the tool performs layout detection of main regions (background, text, images, separators
and marginals).
@ -124,7 +135,7 @@ The command-line interface for binarization can be called like this:
eynollah binarization \
-i <single image file> | -di <directory containing image files> \
-o <output directory> \
-m <directory containing model files> \
-m <directory containing model files>
```
### OCR
@ -138,9 +149,24 @@ eynollah ocr \
-i <single image file> | -di <directory containing image files> \
-dx <directory of xmls> \
-o <output directory> \
-m <directory containing model files> | --model_name <path to specific model> \
-m <directory containing model files> | --model_name <path to specific model>
```
The following options can be used to further configure the ocr processing:
| option | description |
|-------------------|:------------------------------------------------------------------------------- |
| `-dib` | directory of bins(files type must be '.png'). Prediction with both RGB and bins. |
| `-doit` | Directory containing output images rendered with the predicted text |
| `--model_name` | Specific model file path to use for OCR |
| `-trocr` | transformer ocr will be applied, otherwise cnn_rnn model |
| `-etit` | textlines images and text in xml will be exported into output dir (OCR training data) |
| `-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 |
| `-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 |
### Machine-based-reading-order
The machine-based reading-order module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.