基于注意力的端到端离线手写识别的双向解码器网络

P. Doetsch, Albert Zeyer, H. Ney
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引用次数: 23

摘要

递归神经网络可以在序列学习任务上进行端到端训练,与传统的识别系统相比,它有很大的优势。在本文中,我们展示了一种基于注意力的长短期记忆解码器网络在离线手写识别中的应用,并分析了该模型产生的分割、分类和解码错误。我们进一步扩展了解码网络的双向拓扑和一个集成的长度估计过程,并表明它优于单向解码网络。给出了RIMES手写识别数据库的单词和文本行识别任务的结果。实验中使用的软件可免费用于学术研究目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition
Recurrent neural networks that can be trained end-to-end on sequence learning tasks provide promising benefits over traditional recognition systems. In this paper, we demonstrate the application of an attention-based long short-term memory decoder network for offline handwriting recognition and analyze the segmentation, classification and decoding errors produced by the model. We further extend the decoding network by a bidirectional topology together with an integrated length estimation procedure and show that it is superior to unidirectional decoder networks. Results are presented for the word and text line recognition tasks of the RIMES handwriting recognition database. The software used in the experiments is freely available for academic research purposes.
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