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Feature Selection for DNN-HMM Based Mongolian Offline Handwriting Recognition

机译：基于DNN-HMM的蒙古脱机手写识别功能选择

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摘要

The purpose of this paper is to assess the performance of several popular features for handwriting on Mongolian offline handwriting recognition system (HWR). They have been classified into handcrafted and automatically learned features. The handcrafted features are distribution feature, concavity feature, local gradient histogram(LGH) feature and transforms feature. The automatically learned features are extracted by Restricted Boltzmann Machine (RBM) and autoencoder. In this paper, the handwriting recognition system is based on hybrid architectures of hidden Markov models (HMMs)-deep neural networks (DNN) which play state of art role on speech recognize (ASR) tasks. In order to performance comparison, several experiments based on different features extracted from MHW database were performed. The best system on word error rate is based on the LGH feature (5.90%), followed by the autoencoder feature (6.42%).
机译：本文的目的是评估蒙古脱机手写识别系统（HWR）上的几种流行功能的性能。他们已被分类为手工制作和自动学习的功能。手绘功能是分发功能，凹形功能，本地渐变直方图（LGH）功能和转换功能。自动学习的功能由受限制的Boltzmann机器（RBM）和AutoEncoder提取。在本文中，手写识别系统基于隐马尔可夫模型（HMMS）的混合架构 - DEEP神经网络（DNN），其在语音识别（ASR）任务上播放最新的艺术角色状态。为了性能比较，执行基于从MHW数据库中提取的不同特征的若干实验。单词错误率的最佳系统基于LGH功能（5.90％），然后是AutoEncoder功能（6.42％）。

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