Summary
A visual nervous system inspired approach to optical character recognition is proposed in this paper with the hope to touch human performance in a limited extent. Particularly, the application of features motivated by the hierarchical structure of the visual ventral stream for recognition of both English and Persian handwritten digits is investigated. Features are derived by combining position and scale invariant edge detectors in a hierarchy over neighboring positions and multiple orientations. The extracted features are then used to train and test a classifier. We examine three types of classifiers: ANN, SVM and kNN to show that features are not dependent on a specific classifier which is in support of these features. The evaluation of the proposed method over standard Persian and English handwritten digit datasets shows high recognition rates of 99.63% and 98.9%, respectively. A stability analysis is also performed to demonstrate the robustness of this method to orientation, scale, and translation distortions.
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Optical Character Recognition Motivated by Primate Visual System
1. Introduction
Optical character recognition (OCR) is an interesting problem among pattern recognition community. Besides having popular applications like postal mail sorting, bank checking processing, form data entry etc. research in this area has also contributed to advance other areas of pattern recognition. Due to various practical conditions, robust OCR solutions are needed in today industry. Although many researchers have tried to deal with character recognition from different perspectives to achieve higher accuracy, stability, and speed this problem is still open to further studies. Our approach to this problem is inspired from biology of the visual system.Many different handwritten digit recognition methods have been proposed for languages like English, Chinese, Japanese, and Persian etc. Two of the most successful approaches i...See the full content of this document
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