Metadata-Version: 2.1
Name: ocrd_pc_segmentation
Version: 0.2.2
Summary: pixel-classifier based page segmentation
Home-page: https://github.com/ocr-d-modul-2-segmentierung/ocrd-pixelclassifier-segmentation
Author: Alexander Gehrke, Christian Reul, Christoph Wick
Author-email: alexander.gehrke@uni-wuerzburg.de, christian.reul@uni-wuerzburg.de, christoph.wick@uni-wuerzburg.de
License: UNKNOWN
Description: # page-segmentation module for OCR-d
        
        ## Introduction
        
        This module implements a page segmentation algorithm based on a Fully
        Convolutional Network (FCN). The FCN creates a classification for each pixel in
        a binary image. This result is then segmented per class using XY cuts.
        
        ## Requirements
        
        - For GPU-Support: [CUDA](https://developer.nvidia.com/cuda-downloads) and
          [CUDNN](https://developer.nvidia.com/cudnn)
        - other requirements are installed via Makefile / pip, see `requirements.txt`
          in repository root.
        
        ## Installation
        
        If you want to use GPU support, set the environment variable `TENSORFLOW_GPU`
        to a nonempty value, otherwise leave it unset. Then:
        
        ```bash
        make deps
        ```
        
        to install dependencies and
        
        ```sh
        make install
        ```
        
        to install the package.
        
        Both are python packages installed via pip, so you may want to activate
        a virtalenv before installing.
        
        ## Usage
        
        `ocrd-pc-segmentation` follows the [ocrd CLI](https://ocr-d.github.io/cli).
        
        It expects a binary page image and produces region entries in the PageXML file.
        
        ## Configuration
        
        The following parameters are recognized in the JSON parameter file:
        
        - `overwrite_regions`: remove previously existing text regions
        - `xheight`: height of character "x" in pixels used during training.
        - `model`: pixel-classifier model path. The special values `__DEFAULT__` and `__LEGACY__` load the bundled default model or previous default model respectively.
        - `gpu_allow_growth`: required for GPU use with some graphic cards
          (set to true, if you get CUDNN_INTERNAL_ERROR)
        - `resize_height`: scale down pixelclassifier output to this height before postprocessing. Independent of training / used model.
          (performance / quality tradeoff, defaults to 300)
        
        ## Testing
        
        There is a simple CLI test, that will run the tool on a single image from the assets repository.
        
        `make test-cli`
        
        ## Training
        
        To train models for the pixel classifier, see [its README](https://github.com/ocr-d-modul-2-segmentierung/page-segmentation/blob/master/README.md)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Description-Content-Type: text/markdown
Provides-Extra: tf_gpu
Provides-Extra: tf_cpu
