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Vector-attribute character recognition

Bouma, S. and Buck, W.E.S.M. and Moelker, R.R. (2011) Vector-attribute character recognition. Bachelor's Thesis, Computing Science.

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Abstract

Traditional optical character recognition (OCR) uses thresholding and pre- defined characters to recognize text. This study makes the assumption that thresholding can cause loss of potentially valuable information. A more ver- satile approach to character recognition with minimal thresholding is offered which relies on a max-tree and image moments. The max-tree is a tree repre- sentation of the image where each level represents a grey-level and each node contains attributes. The image moments are scale and translational invariant, this allows recognition of a wide range of characters instances. Finally super- vised learning is used to classify the characters, and line and word segmentation is used to obtain a full textual reconstruction of an input image.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Computing Science
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 07:46
Last Modified: 15 Feb 2018 07:46
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/9727

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