Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification

This repository provides the code for our paper titled "Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification" Accepted at Asian Conference on Pattern Recognition 2021(arxiv version)

2.) Overview

2.1.)Introduction

Text independent writer identification is a challenging problem that differentiates between different handwriting styles to decide the author of the handwritten text. Earlier writer identification relied on handcrafted features to reveal pieces of differences between writers. Recent work with the advent of convolutional neural network, deep learning-based methods have evolved. In this paper, three different deep learning techniques - spatial attention mechanism, multi-scale feature fusion and patch-based CNN were proposed to effectively capture the difference between each writer's handwriting. Our methods are based on the hypothesis that handwritten text images have specific spatial regions which are more unique to a writer's style, multi-scale features propagate characteristic features with respect to individual writers and patch-based features give more general and robust representations that helps to discriminate handwriting from different writers. The proposed methods outperforms various state-of-the-art methodologies on word and page-level writer identification methods on the CVL, Firemaker, CERUG-EN datasets and give comparable performance on the IAM dataset.

2.2.) Spatial Attention Unit in SA-Net

2.3.) MSRF-Classification Network Architecture

2.4.) PatchNet Architecture

3.) Training and Testing

3.1)Data Preparation

The code for downloading and using it for training and testing is embedded in python train.py for CERUG-EN and Firemaker Dataset,the training and testing split for IAM dataset is provided in IAM-train.txt and IAM-test.txt

3.2)Training

The architecture for MSRF-CNet, SA-Net is defined in msrfc.py and PatchNet architecture is in patchnet.py, for training change the dataset as required in the train.py, the testing code is also in train.py, enjoy! Run the script as: python train.py

4.) Citation

Please cite our paper if you find the work useful:

@article{srivastava2021exploiting,
  title={Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification},
  author={Srivastava, Abhishek and Chanda, Sukalpa and Pal, Umapada},
  journal={arXiv preprint arXiv:2111.10605},
  year={2021}
}

5.) FAQ

Please feel free to contact me if you need any advice or guidance in using this work (E-mail)