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Author: search.filters.author.Lee, HJSubject: search.filters.subject.character segmentation

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    Chinese handwritten character segmentation in form documents
    (1999)
    Chen, JL
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    Wu, CH
    ;
    Lee, HJ
    ;
    資訊工程學系
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    Department of Computer Science
    This paper presents a projection based method for segmenting handwritten Chinese characters in form documents with known structures. In the preprocessing phase, a noise removal method is proposed that preserves stroke connections and character edge points. In the character segmentation phase, the projection profile analysis method is used to segment a text line image into projection blocks. In addition, projection blocks are classified into one of four types: mark, half-word, single-word, and two-word. Large blocks are then split and small blocks are merged. In addition, an OCR system is adopted to eliminate errors resulting from the inappropriate merging of Chinese numerical characters with other characters. As for 1319 Chinese characters are tested during our experiments, the correct segmentation rates of 92.34% and 91.76% are obtained with and without the OCR module.
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    Design of a mathematical expression understanding system
    (1997-03-01)
    Lee, HJ
    ;
    Wang, JS
    ;
    交大名義發表
    ;
    資訊工程學系
    ;
    National Chiao Tung University
    A scientific document usually consists of text and mathematical expressions. In this paper, we present a system for segmenting and understanding text and mathematical expressions in a document, The system can be divided into six stages: page segmentation and labeling, character segmentation, feature extraction, character recognition, expression formation, and error correction and expression extraction. After we extract all text lines in a document, we separate all symbols in each text line and calculate direction-feature vectors and aspect ratios for those symbols. Then, a nearest-neighbor algorithm recognizes characters. In the expression formation stage, we build a symbol relation tree for each text line that represents the relationships among the symbols in the text line. Each text line is decomposed into a collection of primitive tokens: operands, operators and separators. Heuristic rules based on these primitive tokens are used to correct text recognition errors. Finally, we extract all mathematical expressions according to basic expression forms. Several pages of documents were scanned to test the method. All mathematical expressions are understood. In the expressions generated, a few symbols are misrecognized. The average recognition rate was 96.16%. (C) 1997 Elsevier Science B.V.