Abstract
This paper presents a new method to recognize machine-printed traditional Mongolian characters by using back-propagation (BP) neural networks. First, the set of traditional Mongolian characters is divided into five subsets according to each character's position (initial, medial or final) within a word and some steady structural features. Then, each subset is trained and recognized by using a BP neural network with particular architecture. Thus, there are five BP neural networks in total, which can recognize all the traditional Mongolian characters. The experimental results are provided and show that the BP neural networks have good performance for the machine-printed traditional Mongolian characters recognition.
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