MLP-based Assamese Character and Numeral Recognition using an Innovative Hybrid Feature Set
نویسنده
چکیده
Neural number recognition as an extension of an Optical Character Recognition (OCR) system requires a unique feature set capturing relevant details of the input profiles. The performance of such a recognition system depends on the feature set. The work deals with the design of a combined character and numeral recognition system using a hybrid feature set applied earlier to the recognition of Assamese characters. The work also describes the use of a modified hybrid feature set derived for handwritten numeral detection using a neural network. The set aims to maximize recognition performance, improve robustness and invariance to shape and size in presence of noise.
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تاریخ انتشار 2007