Towards written text recognition based on handwriting experiences using a recurrent neural network

Shun Nishidea, Jun Tanib, Hiroshi G. Okuno, Tetsuya Ogataa

研究成果: Article

1 引用 (Scopus)

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In this paper, we propose a model for recognizing written text through prediction of a handwriting sequence. The approach is based on findings in the brain sciences field. When recognizing written text, humans are said to unintentionally trace its handwriting sequence in their brains. Likewise, we aim to create a model that predicts a handwriting sequence from a static image of written text. The predicted handwriting sequence would be used to recognize the text. As the first step towards the goal, we created a model using neural networks, and evaluated the learning and recognition capability of the model using single Japanese characters. First, the handwriting image sequences for training are self-organized into image features using a self-organizing map. The self-organized image features are used to train the neuro-dynamics learning model. For recognition, we used both trained and untrained image sequences to evaluate the capability of the model to adapt to unknown data. The results of two experiments using 10 Japanese characters show the effectivity of the model.

元の言語English
ページ(範囲)2173-2187
ページ数15
ジャーナルAdvanced Robotics
25
発行部数17
DOI
出版物ステータスPublished - 2011
外部発表Yes

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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