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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)2173-2187
Number of pages15
JournalAdvanced Robotics
Issue number17
Publication statusPublished - 2011
Externally publishedYes


  • Neural networks
  • prediction-based recognition
  • self-organizing map
  • text recognition

ASJC Scopus subject areas

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


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