A Low-Complexity Constructive Learning Automaton Approach to Handwritten Character Recognition
نویسندگان
چکیده
The task of syntactic pattern recognition has aroused the interest of researchers for several decades. The power of the syntactic approach comes from its capability in exploiting the sequential characteristics of the data. In this work, we propose a new method for syntactic recognition of handwritten characters. The main strengths of our method are its low run-time and space complexity. In the lexical analysis phase, the lines of the presented sample are segmented into simple strokes, which are matched to the primitives of the alphabet. The reconstructed sample is passed to the syntactic analysis component in the form of a graph where the edges are the primitives and the vertices are the connection points of the original strokes. In the syntactic analysis phase, the interconnections of the primitives extracted from the graph are used as a source for the construction of a learning automaton. We reached recognition rates of 72% for the best match and 94% for the top five matches.
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