Package: ocr4all-pixel-classifier-frontend
OCR4All Pixel ClassifierRequirementsPython dependencies are specified inrequirements.txt/setup.py.You must install the package via pip with eitherocr4all_pixel_classifier[tf_cpu]to
use CPU version of tensorflow orocr4all_pixel_classifier[tf_gpu]to use GPU (CUDA)
version of tensorflow. For the latter, your system should be set up with CUDA 9
and CuDNN 7.UsagePixel classifierClassificationTo run a model on some input images, useocr4all-pixel-classifier predict:ocr4all-pixel-classifierpredict--loadPATH_TO_MODEL\--outputOUTPUT_PATH\--binaryPATH_TO_BINARY_IMAGES\--imagesPATH_TO_SOURCE_IMAGES\--normPATH_TO_NORMALIZATIONS(ocr4all-pixel-classifieris an alias forocr4all-pixel-classifier predict)This will create three folders at the output path:color: the classification as color image, with pixel color corresponding to
the class for that pixelinverted: inverted binary image with classification of foreground pixels
only (i.e. background is black, foreground is white or class color)overlay: classification image layered transparently over the original imageTrainingFor training, you first have to create dataset files. A dataset file is a JSON
file containing three arrays, for train, test and evaluation data (also
called train/validation/test in other publications). The JSON file uses the
following format:{"train":[//datasets here],"test":[//datasets here],"eval":[//datasets here]}A dataset describes a single input image and consists of several paths: the
original image, a binarized version and the mask (pixel color corresponds to
class). Furthermore, the line height of the page in pixels must be specified:{"binary_path":"/path/to/image/binary/filename.bin.png","image_path":"/path/to/image/color/filename.jpg","mask_path":"/path/to/image/mask/filename_MASK.png","line_height_px":18}The generation of dataset files can be automated usingocr4all-pixel-classifier create-dataset-file. Refer to the command's--helpoutput for further
information.To start the training:ocr4all-pixel-classifiertrain\--trainDATASET_FILE.json--testDATASET_FILE.json--evalDATASET_FILE.json\--outputMODEL_TARGET_PATH\--n_iter5000The parameters--train,--testand--evalmay be followed by any number of
dataset files or patterns (shell globbing).Refer toocr4all-pixel-classifier train --helpfor further parameters provided to
affect the training procedure.You can combine several dataset files into asplit file. The format of the
split file is:{"label":"name of split","train":["/path/to/dataset1.json","/path/to/dataset2.json",...],"test":[//dataset paths here],"eval":[//dataset paths here]}To use a split file, add the--split_fileparameter.ExamplesSee the examples fordataset generationandtrainingocr4all-pixel-classifier compute-image-normalizations/ocrd_compute_normalizationsCalculate image normalizations, i.e. scaling factors based on average line
height.Required arguments:--input_dir: location of images--output_dir: target location of norm filesOptional arguments:--average_all: Average height over all images--inverseVersioningMajor and minor versions are usually identical to the ones of the usedocr4all-pixel-classifierlibrary. As this package is a frontend package and not
intended for use as library, no guarantees are made regarding API stability
between versions. Useocr4all-pixel-classifierin this case.