Handwriting prediction based character recognition using recurrent neural network
Abstract
Humans are said to unintentionally trace handwriting sequences in their brains based on handwriting experiences when recognizing written text. In this paper, we propose a model for predicting handwriting sequence for written text recognition based on handwriting experiences. The model is first trained using image sequences acquired while writing text. The image features of sequences are self-organized from the images using Self-Organizing Map. The feature sequences are used to train a neuro-dynamics learning model. For recognition, the text image is input into the model for predicting the handwriting sequence and recognition of the text. We conducted two experiments using ten Japanese characters. The results of the experiments show the effectivity of the model.
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FSMs may not capture complex or dynamic behaviors accurately.
“It's worth noting that while FSMs are effective for modeling certain types of systems, they may not capture complex or dynamic behaviors accurately.”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [177]
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References 12 · Cited by 13
- Authors
- Nishide, S., Okuno, H. G., Ogata, T., & Tani, J.
- Affiliations
- Kyoto University (JP)
- RIKEN Center for Brain Science (JP)
- Venue
- 2011 IEEE International Conference on Systems, Man, and Cybernetics
- Publisher
- IEEE
- Published
- 2011-10
- Pages
- 2549-2554
- ISSN
- 1062-922X (ISSN-L)
- OpenAlex
- W2076316011
- Cited by
- 13
- References
- 12
- Citations by year
- 2025: 1 · 2023: 1 · 2021: 3 · 2020: 3 · 2019: 1 · 2018: 1 · 2017: 1 · 2015: 2
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (publishedVersion)
- Funders
- Precursory Research for Embryonic Science and Technology
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Media Technology › Vehicle License Plate Recognition
- Keywords
- character recognition, handwriting recognition, recurrent neural networks, self-organizing maps, Japanese characters, image sequences
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