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

EasyChair Preprint 7085

Abstract

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 differences between writers. Recently 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 . We outperform various state-of-the-art methodologies on word and page-level writer identification methods on CVL, Firemaker, CERUG-EN datasets and give comparable performance on the IAM dataset.

Keyphrases: Convolutional Neural Networks, MSRF-Net, writer identification

@booklet{EasyChair:7085,
  author    = {Abhishek Srivastava and Sukalpa Chanda and Umapada Pal},
  title     = {Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification},
  howpublished = {EasyChair Preprint 7085},
  year      = {EasyChair, 2021}}