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.

翻译：图神经网络已被证明是实际应用中的高效机器学习技术。手写识别是现实生活中的重要应用领域，同时需要离线与在线手写识别。链码作为特征提取技术在文献中展现出显著效果，我们成功将链码与图神经网络相结合。据我们所知，本工作首次提出手写轨迹特征（链码）与图神经网络的新型组合方案。对于离线手写文本的轨迹，我们通过绘制顺序恢复进行评估，而在线手写轨迹则直接采用链码处理。实验结果表明，本组合方案在少量训练周期内即超越既往成果，并有效降低错误率。