“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 9585 |
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Humans are very good at recognizing alphanumeric characters. They show much greater accuracy, robustness and speed compared with best engineering methods yet known for this task. A visual nervous system inspired approach to optical character recognition is studied in this paper with the hope to touch its performance on a limited extent. Particularly application of features motivated from the hierarchical structure of the visual ventral stream for recognition of Persian (Arabic) handwritten digits is investigated. A set of position- and scale-invariant edge detectors is combined over neighboring positions and multiple orientations to build more complex features. Extracted features are then used to train and test a classifier for recognition of handwritten digits. We examined three types of classifiers: ANN, SVM and kNN to show that features are not dependent on a specific classifier which is evidence toward strength of these features for our purpose. Evaluation of this method over standard Persian handwritten digits dataset shows a high recognition rate of 99.63
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