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/sbb_pixelwise_segmentation/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. Our lables are 3 channel png images but only information of first channel is used. If you have an image label with height and width of 10, for a binary case the first channel should look like this:

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

This means that you have an image by 10*10*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.

Parameter configuration

• patches: If you want to break input images into smaller patches (input size of the model) you need to set this parameter to true. In the case that the model should see the image once, like page extraction, patches should be set to false.
• n_batch: Number of batches at each iteration.
• n_classes: Number of classes. In the case of binary classification this should be 2.
• n_epochs: Number of epochs.
• input_height: This indicates the height of model's input.
• input_width: This indicates the width of model's input.
• weight_decay: Weight decay of l2 regularization of model layers.
• augmentation: If you want to apply any kind of augmentation this parameter should first set to true.
• flip_aug: If true, different types of filp will applied on image. Type of flips is given by "flip_index" in train.py file.