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@ -14,7 +14,7 @@ Alternatively, you can also use `make` with these targets:
`make install-dev` for editable installation
The current version of Eynollah runs on Python >=3.6 with Tensorflow >=2.4.
The current version of Eynollah runs on Python `>=3.6` with Tensorflow `>=2.4`.
In order to use a GPU for inference, the CUDA toolkit version 10.x needs to be installed.
@ -36,23 +36,23 @@ The command-line interface can be called like this:
eynollah -i <image file name> -o <directory to write output> -m <directory of models> [OPTIONS]
```
Additionally, the following optional parameters can be used to further configure the processing:
The following options can be used to further configure the processing:
```sh
-fl: the tool will perform full layout analysis including detection of marginalia and drop capitals
-ae: the tool will resize and enhance the image. The rescaled and enhanced image is saved to the output directory
-as: the tool will check whether the document needs rescaling or not
-cl: the tool will extract contours of curved textlines instead of rectangle bounding boxes
-si <directory>: when a directory is given here, the tool will save image regions detected in documents to this directory
-sd <directory>: when a directory is given, deskewed image will be saved to this directory
-sa <directory>: when a directory is given, plots of layout detection are saved to this directory
-tab: the tool will try to detect tables
-ib: the tool will binarize the input image
-ho: the tool will ignore headers in reading order detection
-sl <directory>: when a directory is given, plots of layout detection are saved to this directory
-ep: the tool will save a plot. This should be used alongside with `-sl`, `-sd`, `-sa`, `-si` or `-ae` options
-light: the tool will apply a faster method for main region detection and deskewing
-di <directory>: the tool will process all images in the directory in batch mode
-fl: perform full layout analysis including detection of marginalia and drop capitals
-ae: allow resizing and enhancing the input image, a rescaled and enhanced image is saved to the output directory
-as: allow scaling - check whether the input image needs rescaling or not
-cl: extract contours of curved textlines instead of rectangle bounding boxes
-si <directory>: save image regions detected in documents to this directory
-sd <directory>: save deskewed image to this directory
-sa <directory>: save plot of layout detection to this directory
-tab: try to detect tables
-ib: allow binarization of the input image
-ho: ignore headers in reading order detection
-sl <directory>: save plots of layout detection to this directory
-ep: save a plot. This should be used alongside with `-sl`, `-sd`, `-sa`, `-si` or `-ae` options
-light: apply a faster but simpler method for main region detection and deskewing
-di <directory>: process all images in a directory in batch mode
```
The tool performs better with RGB images as input than with greyscale or binarized images.