Package: ocrd-anybaseocr
Document Preprocessing and SegmentationTools to preprocess and segment scanned images for OCR-DInstallingToolsBinarizerDeskewerCropperDewarperText/Non-Text SegmenterBlock SegmenterTextline SegmenterDocument AnalyserTestingLicenseInstallingRequires Python >= 3.6.Create a newvenvunless you already have onepython3 -m venv venvActivate thevenvsource venv/bin/activateTo install from source, get GNU make and do:make installThere are also prebuilds available on PyPI:pip install ocrd_anybaseocr(This will install both PyTorch and TensorFlow, along with their dependents.)ToolsAll tools, also calledprocessors, abide by theCLI specificationsforOCR-D, which roughly looks like:ocrd-<processor-name> [-m <path to METs input file>] -I <input group> -O <output group> [-p <path to parameter file>]* [-P <param name> <param value>]*BinarizerMethod BehaviourFor each page (or sub-segment), this processor takes a scanned colored / gray scale document image as input and computes a binarized (black and white) image.Implemented via rule-based methods (percentile based adaptive background estimation in Ocrolib).Exampleocrd-anybaseocr-binarize -I OCR-D-IMG -O OCR-D-BIN -P operation_level line -P threshold 0.3DeskewerMethod BehaviourFor each page (or sub-segment), this processor takes a document image as input and computes the skew angle of that. It also annotates a deskewed image.The input images have to be binarized for this module to work.Implemented via rule-based methods (binary projection profile entropy maximization in Ocrolib).Exampleocrd-anybaseocr-deskew -I OCR-D-BIN -O OCR-D-DESKEW -P maxskew 5.0 -P skewsteps 20 -P operation_level pageCropperMethod BehaviourFor each page, this processor takes a document image as input and computes the border around the page content area (i.e. removes textual noise as well as any other noise around the page frame). It also annotates a cropped image.The input image need not be binarized, but should be deskewed for the module to work optimally.Implemented via rule-based methods (gradient-based line segment detection and morphology based textline detection).Example:ocrd-anybaseocr-crop -I OCR-D-DESKEW -O OCR-D-CROP -P rulerAreaMax 0 -P marginLeft 0.1DewarperMethod BehaviourFor each page, this processor takes a document image as input and computes a morphed image which will make the text lines straight if they are curved.The input image has to be binarized for the module to work, and should be cropped and deskewed for optimal quality.Implemented via data-driven methods (neural GAN conditional image model trained with pix2pixHD/Pytorch).Modelsocrd resmgr download ocrd-anybaseocr-dewarp '*'Exampleocrd-anybaseocr-dewarp -I OCR-D-CROP -O OCR-D-DEWARP -P resize_mode none -P gpu_id -1Text/Non-Text SegmenterMethod BehaviourFor each page, this processor takes a document image as an input and computes two images, separating the text and non-text parts.The input image has to be binarized for the module to work, and should be cropped and deskewed for optimal quality.Implemented via data-driven methods (neural pixel classifier model trained with Tensorflow/Keras).Modelsocrd resmgr download ocrd-anybaseocr-tiseg '*'Exampleocrd-anybaseocr-tiseg -I OCR-D-DEWARP -O OCR-D-TISEG -P use_deeplr trueBlock SegmenterMethod BehaviourFor each page, this processor takes the raw document image as an input and computes a text region segmentation for it (distinguishing various types of text blocks).The input image need not be binarized, but should be deskewed for the module to work optimally.Implemented via data-driven methods (neural Mask-RCNN instance segmentation model trained with Tensorflow/Keras).Modelsocrd resmgr download ocrd-anybaseocr-block-segmentation '*'Exampleocrd-anybaseocr-block-segmentation -I OCR-D-TISEG -O OCR-D-BLOCK -P active_classes '["page-number", "paragraph", "heading", "drop-capital", "marginalia", "caption"]' -P min_confidence 0.8 -P post_process trueTextline SegmenterMethod BehaviourFor each page (or region), this processor takes a cropped document image as an input and computes a textline segmentation for it.The input image should be binarized and deskewed for the module to work.Implemented via rule-based methods (gradient and morphology based line estimation in Ocrolib).Exampleocrd-anybaseocr-textline -I OCR-D-BLOCK -O OCR-D-LINE -P operation_level regionDocument AnalyserMethod BehaviourFor the whole document, this processor takes all the cropped page images and their corresponding text regions as input and computes the logical structure (page types and sections).The input image should be binarized and segmented for this module to work.Implemented via data-driven methods (neural Inception-V3 image classification model trained with Tensorflow/Keras).Modelsocrd resmgr download ocrd-anybaseocr-layout-analysis '*'Exampleocrd-anybaseocr-layout-analysis -I OCR-D-LINE -O OCR-D-STRUCTTestingTo test the tools under realistic conditions (on OCR-D workspaces),
downloadOCR-D/assets. In particular,
the code is tested with thedfki-testdatadataset.To download the data:make assetsTo run module tests:make testTo run processor/workflow tests:make cli-testLicenseLicensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
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     http://www.apache.org/licenses/LICENSE-2.0

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