Feature extraction based on fuzzy set theory for handwriting recognition

The paper presents a method based on fuzzy set theory for extracting features from handwritten words. After the feature extraction and word segmentation process, a handwritten word is represented by an ordered sequence of line segments. For each of these segments, membership values for fuzzy sets ar...

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Bibliographic Details
Main Authors: Rodrigues Gomes, N., Lee Luan Ling
Format: Conference Proceeding
Language:English
Subjects:
Feature extraction
Fuzzy set theory
Fuzzy sets
Handwriting recognition
Hidden Markov models
Image recognition
Image segmentation
System testing
Vocabulary
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Summary:The paper presents a method based on fuzzy set theory for extracting features from handwritten words. After the feature extraction and word segmentation process, a handwritten word is represented by an ordered sequence of line segments. For each of these segments, membership values for fuzzy sets are calculated, representing different types of curved lines and straight lines. The position of the line segments in a letter or piece of a letter resulting from the word segmentation is also evaluated by means of fuzzy sets. Fuzzy hidden Markov models are employed to classify the handwritten words. A database comprising handwritten words extracted from Brazilian bank checks is used to test the proposed system.
DOI:10.1109/ICDAR.2001.953871