Abstract
This paper presents an effective automated analysis system for mixed documents consisting of handwritten texts and graphic images. In the preprocessing step, an input image is binarized, then graphic regions are separated from text parts using chain codes of connected components. In the character recognition step, we recognize two different sets of handwritten characters: Korean and alphanumeric characters. Considering the structural complexity and variations of Korean characters, we separate them based on partial recognition results of vowels and extract primitive phonemes using a branch and bound algorithm based on dynamic programming (DP) matching. Finally, to validate recognition results, a dictionary and knowledge are employed. Computer simulation with 50 test documents shows that the proposed algorithm analyzes effectively mixed documents.
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