A Comparison between Two Character Recognition Approaches
نویسنده
چکیده
This paper presents the architecture of an Optical Character Recognition (OCR) technology application based on two approaches, a multilayer neural network and a Support Vector Machine (SVM) classifier using Zernike moments for feature extraction. The performance comparison of the two approaches is based on the similar layout of most of the characters that must be recognized. The comparison shows that the improvement of the processing performance can be obtained by creating classes of blobs that use geometric similarities, and doing OCR only on the representative blob from each class.
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تاریخ انتشار 2012