From d98689edad30cba72749d3abee65d908e6980282 Mon Sep 17 00:00:00 2001 From: cneud <952378+cneud@users.noreply.github.com> Date: Fri, 14 Apr 2023 03:13:07 +0200 Subject: [PATCH] Update README.md --- README.md | 39 ++++++++++++++++++--------------------- 1 file changed, 18 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index 7ce0782..dd4324a 100644 --- a/README.md +++ b/README.md @@ -13,14 +13,14 @@ * background, page border, text region, text line, header, image, separator, marginalia, initial (drop capital), table * Support for various image optimization operations: * cropping (border detection), binarization, deskewing, dewarping, scaling, enhancing, resizing -* Text line segmentation to bounding boxes or polygons (contours) including curved lines and vertical text +* Text line segmentation to bounding boxes or polygons (contours) including for curved lines and vertical text * Detection of reading order -* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) format +* Output in [PAGE-XML](https://github.com/PRImA-Research-Lab/PAGE-XML) ## Installation Python versions `3.7-3.10` with Tensorflow `>=2.4` are currently supported. -For (minimal) GPU support the [matching](https://www.tensorflow.org/install/source#gpu) CUDA toolkit `>=10.1` needs to be installed. +For (limited) GPU support the [matching](https://www.tensorflow.org/install/source#gpu) CUDA toolkit `>=10.1` needs to be installed. You can either install via @@ -43,8 +43,6 @@ Pre-trained models can be downloaded from [qurator-data.de](https://qurator-data Alternatively, running `make models` will download and extract models to `$(PWD)/models_eynollah`. -### Training - In case you want to train your own model to use with Eynollah, have a look at [sbb_pixelwise_segmentation](https://github.com/qurator-spk/sbb_pixelwise_segmentation). ## Usage @@ -61,22 +59,21 @@ eynollah \ The following options can be used to further configure the processing: -``` --fl perform full layout analysis including detection of headers and drop capitals --tab try to detect tables --light apply a faster but simpler method for main region detection and deskewing --ae allow resizing and enhancing the input image, the enhanced image is saved to the output directory --as allow scaling - automatically check whether the input image needs scaling or not --ib allow binarization of the input image --ho ignore headers for reading order prediction --cl extract contours of curved textlines instead of rectangle bounding boxes --ep enables plotting. This MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae` options --di process all images in a directory in batch mode --si save image regions detected in documents to this directory --sd save deskewed image to this directory --sl save layout prediction as plot to this directory --sa save all outputs (plot, enhanced or binary image and layout prediction) to this directory -``` +| option | description | +|----------|:-------------| +| `-fl` | apply full layout analysis including all steps and segmentation classes | +| `-light` | apply a lighter and faster but simpler method for main region detection and deskewing | +| `-tab` | apply table detection | +| `-ae` | apply enhancement (the resulting image is saved to the output directory) | +| `-as` | apply scaling | +| `-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 | +| `-di ` | process all images in a directory in batch mode | +| `-si ` | save image regions detected in documents to this directory | +| `-sd ` | save deskewed image to this directory | +| `-sl ` | save layout prediction as plot to this directory | +| `-sa ` | save all (plot, enhanced, binary image and layout prediction) to this directory | The tool performs better with RGB images as input than with greyscale or binarized images.