Comparison of Structure Based Models for Handwritten English Character Recognition
DOI:
https://doi.org/10.26438/ijcse/v5i8.126130Keywords:
Character recognition, stroke detector, codewords, spatially embedded dictionary, part based modelAbstract
Characters are the symbols made by man that are composed of different structure and strokes for easy communication. The intrinsic characteristics of the characters can be utilized to design the stroke and structure based models for handwritten character recognition. This paper focus to learn the part based and the stroke detector based models to recognize the characters by detecting the elastic strokes. The Tree Structured Model (TSM) and the Mixture of parts Tree Structured Model (MTSM) are the part based models that uses the trained part models on the images to recognize the characters. These models require manually labelled key points. In order to learn the discriminative stroke detectors automatically, the discriminative spatiality embedded dictionary learning-based representation (DSEDR) is used for character recognition. A comparative study is made on all the three models on the chars74k dataset to determine the model that shows the best performance.
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