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Preparation of an Unconstrained Vietnamese Online Handwriting Database and Recognition Experiments by Recurrent Neural Networks

机译:经常性神经网络的编制编制无约会越南网上手写数据库和识别实验

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This paper presents our attempts to collect and analyze unconstrained Vietnamese online handwriting text patterns by pen-based computers. Totally, our database contains over 120,000 strokes from more than 140,000 characters, which is one of the largest Vietnamese online handwriting pattern databases currently. For building and analyzing our database, we made a collection tool, a line segmentation tool, and a delayed stroke detection tool. Moreover, we investigated some statistical information from personal information of writers. In order to solve the unconstrained handwriting recognition problem, we conducted experiments using Bidirectional Long Short-Term Memory (BLSTM) networks. BLSTM network is architecture of Recurrent Neural Network (RNN) and applied recently for many related problems. The performance of BLSTM network on our database is nearly 80% of accuracy even though this database contains many delayed strokes. In near future, we are going to avail our database for research purposes, as it would be the fundamental for the handwriting recognition research.
机译:本文介绍我们试图通过笔的计算机收集和分析无约会的越南网手写文本模式。完全,我们的数据库包含超过140,000个字符的120,000多个笔划,这是目前最大的越南在线手写模式数据库之一。用于构建和分析我们的数据库,我们制作了一个集合工具,行分段工具和延迟笔划检测工具。此外,我们研究了作者个人信息的一些统计信息。为了解决不受约束的手写识别问题,我们使用双向短期内记忆(BLSTM)网络进行实验。 BLSTM网络是经常性神经网络(RNN)的架构,最近应用了许多相关问题。即使此数据库包含许多延迟笔划,BLSTM网络对我们数据库上的BLSTM网络的性能近于80%的准确性。在不久的将来,我们将利用我们的数据库进行研究目的,因为它将是手写识别研究的基础。

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