Pixelwise Segmentation

Pixelwise segmentation for document images

Introduction

This repository contains the source code for training an encoder model for document image segmentation.

Installation

Either clone the repository via git clone https://github.com/qurator-spk/sbb_pixelwise_segmentation.git or download and unpack the ZIP.

Pretrained encoder

Download our pretrained weights and add them to a pretrained_model folder:
https://qurator-data.de/pretrained_encoder/

Usage

Train

To train a model, run: python train.py with config_params.json

Ground truth format

Lables for each pixel are identified by a number. So if you have a binary case, n_classes should be set to 2 and labels should be 0 and 1 for each class and pixel.

In the case of multiclass, just set n_classes to the number of classes you have and the try to produce the labels by pixels set from 0 , 1 ,2 .., n_classes-1. The labels format should be png.

If you have an image label for a binary case it should look like this:

Label: [ [[1 0 0 1], [1 0 0 1] ,[1 0 0 1]], 
[[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ,
[[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ] 

This means that you have an image by 3*4*3 and pixel[0,0] belongs to class 1 and pixel[0,1] belongs to class 0.

Training , evaluation and output

The train and evaluation folders should contain subfolders of images and labels. The output folder should be an empty folder where the output model will be written to.

Patches

If you want to train your model with patches, the height and width of the patches should be defined and also the number of batches (how many patches should be seen by the model in each iteration).

In the case that the model should see the image once, like page extraction, patches should be set to false.