One-dimensional representation of two-dimensional information for HMM based handwriting recognition

Date

2000-06-01

Author

Arica, N
Yarman Vural, Fatoş Tunay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this study, we introduce a one-dimensional feature set, which embeds two-dimensional information into an observation sequence of one-dimensional string, selected from a code-book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for Hidden Markov Model (HMM) based shape recognition schemes. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of HMM. The proposed recognition system is tested on handwritten data of the National Institute of Standards and Technology (NIST) database and a local database. The experimental results indicate very high recognition rates.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/62489

Journal

PATTERN RECOGNITION LETTERS

DOI

https://doi.org/10.1016/s0167-8655(00)00023-4

Collections

Department of Computer Engineering, Article

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Citation Formats

N. Arica and F. T. Yarman Vural, “One-dimensional representation of two-dimensional information for HMM based handwriting recognition,” PATTERN RECOGNITION LETTERS, pp. 583–592, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62489.