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Separation and recognition of connected handprinted capital English characters

University of British Columbia

The subject of machine recognition of connected characters is investigated. A generic single character recognizer (SCR) assumes there is only one character in the image. The goal of this project is to design a connected character segmentation algorithm (CCSA) without the above assumption. The newly designed CCSA will make use of a readily available SCR. The input image (e.g. a word with touching letters) is first transformed (thinned) into its skeletal form. The CCSA will then extract the image features (nodes and branches) and store them in a hierarchical form. The hierarchy stems from the left-to-right rule of writing of the English language. The CCSA will first attempt to recognize the first letter. When this is done, the first letter is deleted and the algorithm repeats. After extracting the image features, the CCSA starts to create a set of test images from the beginning of the word (i.e. beginning of the description). Each test image contains one more feature than its predecessor. The number of test images in the set is constrained by a predetermined fixed width or a fixed total number of features. The SCR is then called to examine each test image. The recognizable test image(s) in the set are extracted. Let each recognizable test image be denoted by C₁. For each C₁, a string of letters C₂, C₃, CL is formed. C₂ is the best recognized test image in a set of test images created after the deletion of C₁ from the beginning of the current word. C₃ through CL are created by the same method. All such strings are examined to determine which string contains the best recognized C₁. Experimental results on test images with two characters yield a recognition rate of 72.66%. Examples with more than two characters are also shown. Furthermore, the experimental results suggested that topologically simple test images can be more difficult to recognize than those which are topologically more complex.

Text

2010-07-21

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http://hdl.handle.net/2429/26747

Electrical and Computer Engineering

University of British Columbia

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