A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
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Artificial Intelligence Review
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A new intelligent segmentation technique is proposed that may be used in conjunction with a neural classifier and a simple lexicon for the recognition of difficult handwritten words. A heuristic segmentation algorithm is initially used to over-segment each word. An Artificial Neural Network (ANN) trained with 32,034 segmentation points is then used to verify the validity of the segmentation points found. Following segmentation, character matrices from each word are extracted, normalized and then passed through a global feature extractor after which a second ANN trained with segmented characters is used for classification. These recognized characters are grouped into words and presented to a variable-length lexicon that utilizes a string processing algorithm to compare and retrieve words with highest confidences. This research provides promising results for segmentation, character and word recognition.