Graph Neural Network based Handwritten Trajectories Recognition

TL;DR

This paper introduces a novel approach combining chain code features with graph neural networks for handwritten trajectory recognition, significantly improving accuracy and reducing error rates in both offline and online handwriting tasks.

Contribution

It is the first to integrate chain code features with graph neural networks for handwritten trajectory recognition, achieving superior performance.

Findings

Outperforms previous methods in accuracy

Reduces error rate in fewer training epochs

Effective for both offline and online handwriting recognition

Abstract

The graph neural networks has been proved to be an efficient machine learning technique in real life applications. The handwritten recognition is one of the useful area in real life use where both offline and online handwriting recognition are required. The chain code as feature extraction technique has shown significant results in literature and we have been able to use chain codes with graph neural networks. To the best of our knowledge, this work presents first time a novel combination of handwritten trajectories features as chain codes and graph neural networks together. The handwritten trajectories for offline handwritten text has been evaluated using recovery of drawing order, whereas online handwritten trajectories are directly used with chain codes. Our results prove that present combination surpass previous results and minimize error rate in few epochs only.