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Graph Neural Network based Handwritten Trajectories Recognition

2024-05-15 11:00:42
Anuj Sharma, Sukhdeep Singh, S Ratna

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.

Abstract (translated)

已经证明,图神经网络在现实生活中应用是有效的机器学习技术。手写识别是现实生活中的一个有用的领域,需要同时进行离线和在线手写识别。作为特征提取技术,链式码在文献中已经显示出显著的成果,我们能够使用图神经网络与链式码一起工作。据我们所知,这项工作首次将手写轨迹特征与链式码和图神经网络相结合,形成了一种新的组合。我们使用恢复绘制顺序来评估手写在线文本的手写轨迹,而在线手写轨迹则直接使用链式码。我们的结果证明,这种结合超出了以前的结果,并且在几轮训练后仅能最小化误差率。

URL

https://arxiv.org/abs/2405.09247

PDF

https://arxiv.org/pdf/2405.09247.pdf


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