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@ -25,9 +25,12 @@
documents using a combination of multiple deep learning models and heuristics; therefore processing can be slow.
## Installation
Python `3.8-3.11` with Tensorflow `<2.13` on Linux are currently supported.
For (limited) GPU support the CUDA toolkit needs to be installed.
A working config is CUDA `11.8` with cuDNN `8.6`.
Python `3.8-3.11` with ONNX Runtime on Linux are currently supported.
For GPU support, NVidia drivers supporting CUDA 12 must be installed.
The runtime dependencies will pull in ONNX, TensorRT and CUDA runtime
libraries (including cuDNN) from PyPI.
You can either install from PyPI
@ -52,6 +55,13 @@ pip install "eynollah[OCR]"
make install EXTRAS=OCR
```
> **Note**: Requirements for OCR are more involved,
> as they may need Tensorflow (with tf-keras) and/or
> Torch (with transformers). Those two frameworks may
> also have conflicting CUDA dependencies. An ONNX
> conversion for these models may be achieved soon.
> :construction:
### Docker
Use
@ -64,7 +74,12 @@ When using Eynollah with Docker, see [`docker.md`](https://github.com/qurator-sp
## Models
Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17727267) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah).
Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17727267) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah).
For fast runtime inference, download the ONNX models.
For finetuning training, download the original (Tensorflow / Torch) models
(and install the `[training]` extra).
For model documentation and model cards, see [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).
@ -83,18 +98,26 @@ Eynollah supports five use cases:
Some example outputs can be found in [`examples.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/examples.md).
The **generic options** shared by all subcommands are:
```sh
-m <directory containing model files>
-mv <model category> <model variant> <model path>
-D <device specifier>
-l <log level>
```
### Layout Analysis
The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading
order using heuristic methods or a [pretrained model](https://github.com/qurator-spk/eynollah#machine-based-reading-order).
Detects layout elements, i.e. regions of various types and text lines,
and determines their reading order using either heuristic methods or a
[pretrained model](https://github.com/qurator-spk/eynollah#machine-based-reading-order).
The command-line interface for layout analysis can be called like this:
```sh
eynollah layout \
eynollah [GENERIC_OPTIONS] layout \
-i <single image file> | -di <directory containing image files> \
-o <output directory> \
-m <directory containing model files> \
[OPTIONS]
```
@ -106,7 +129,7 @@ The following options can be used to further configure the processing:
| `-tab` | apply table detection |
| `-ae` | apply enhancement (the resulting image is saved to the output directory) |
| `-as` | apply scaling |
| `-cl` | apply contour detection for curved text lines instead of bounding boxes |
| `-cl` | apply contour detection for curved text lines, deskewing all regions independently |
| `-ib` | apply binarization (the resulting image is saved to the output directory) |
| `-ep` | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`) |
| `-ho` | ignore headers for reading order dectection |
@ -115,79 +138,93 @@ 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 |
| `-thart` | confidence threshold of artifical boundary class during textline detection |
| `-tharl` | confidence threshold of artifical boundary class during region detection |
| `-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 |
| `-j` | number of CPU jobs to run parallel (useful with -di) |
| `-H` | when to halt when some jobs fail |
The default is to only perform layout detection of main regions
(background, text, images, separators and marginals).
If no further option is set, the tool performs layout detection of main regions (background, text, images, separators
and marginals).
The best output quality is achieved when RGB images are used as input rather than greyscale or binarized images.
The best output quality is achieved when RGB images are used as input
rather than greyscale or binarized images.
Additional documentation can be found in [`usage.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/usage.md).
Additional documentation can be found in
[`usage.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/usage.md).
### Binarization
The binarization module performs document image binarization using pretrained pixelwise segmentation models.
Performs document image binarization (thresholding)
using pretrained pixelwise segmentation models.
The command-line interface for binarization can be called like this:
```sh
eynollah binarization \
eynollah [GENERIC_OPTIONS] binarization \
-i <single image file> | -di <directory containing image files> \
-o <output directory> \
-m <directory containing model files>
[OPTIONS]
```
### Image Enhancement
TODO
This enlarges and enhances images. Useful in case the scan quality is low.
```sh
eynollah [GENERIC_OPTIONS] enhancement \
-i <single image file> | -di <directory containing image files> \
-o <output directory> \
[OPTIONS]
```
| option | description |
|-------------------|:--------------------------------------------------------------------------------------------|
| `-sos` | save the enhanced image in original image size |
| `-ncu` | upper limit of columns in document image |
| `-ncl` | lower limit of columns in document image |
### OCR
The OCR module performs text recognition using either a CNN-RNN model or a Transformer model.
Performs text recognition using either a CNN-RNN model or a Transformer model.
Needs a PAGE-XML input file.
The command-line interface for OCR can be called like this:
```sh
eynollah ocr \
eynollah [GENERIC_OPTIONS] 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>
```
The following options can be used to further configure the ocr processing:
| option | description |
|-------------------|:-------------------------------------------------------------------------------------------|
| `-trocr` | use transformer OCR model instead of CNN-RNN model |
| `-dib` | directory of binarized images (file type must be '.png'), prediction with both RGB and bin |
| `-doit` | directory for output images rendered with the predicted text |
| `--model_name` | file path to use specific model for OCR |
| `-trocr` | use transformer ocr model (otherwise cnn_rnn model is used) |
| `-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 |
| `-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 |
| `-min_conf` | minimum OCR confidence value. OCR with textline conf lower than this will be ignored |
### Reading Order Detection
Reading order detection can be performed either as part of layout analysis based on image input, or, currently under
development, based on pre-existing layout analysis data in PAGE-XML format as input.
The reading order detection module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.
Reading order can be detected either during layout analysis,
or as a separate module, which requires a PAGE-XML input file.
The command-line interface for machine based reading order can be called like this:
```sh
eynollah machine-based-reading-order \
eynollah [GENERIC_OPTIONS] machine-based-reading-order \
-i <single image file> | -di <directory containing image files> \
-xml <xml file name> | -dx <directory containing xml files> \
-m <path to directory containing model files> \
-o <output directory>
```