Abstract
Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents a novel approach based on a combination of MLP and SVM to design a trainable OCR for Persian/Arabic cursive documents. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.
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Keywords
- Support Vector Machine
- Natural Language Processing
- Fuzzy Inference System
- Support Vector Machine Classifier
- Multi Layer Perceptrons
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Pirsiavash, H., Mehran, R., Razzazi, F. (2005). A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_63
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DOI: https://doi.org/10.1007/11578079_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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