A paper which describes thinning, which is an important preprocessing step in Optical Character Recognition.
Written in 2003; 3,857 words; 16 sources; MLA; $ 105.95
Paper Summary:
This paper gives some algorithms for thinning a monochrome image and checks out the development in other algorithms. It explains that thinning is an important preprocessing step for many image analysis operations such as optical character recognition, fingerprint recognition and document processing. This paper studies the results of some of the various popularly implemented thinning algorithms like medial axis method [2], contour generation method [3], local thickness based thinning approach [4], hybrid Approach and sequential and parallel thinning [5], and to debate on the advantages and disadvantages of the different methods along with citing the places where each of them is optimally utilized.
From the Paper:
"Communication is an integral component of human society that has been greatly enhanced by the evolution of the technological age. Verbal communication has been greatly revolutionized by the advent of television, telephone and radio, and written communication has been transformed by development of word processors and electronic data. It is hence becoming increasing important to have information available for examination and manipulation in the digital format, and Optical Character Recognition (OCR) methods have been created towards this end. Character Recognition or Optical Character Optical Character Recognition (OCR) is the process of converting scanned images of machine printed or handwritten text (numerals, letters, and symbols), into a computer process able format (such as ASCII). The recognition of optically processed symbols and numerals, that is their translation from human readable forms to machine codes, holds excessive practical applicability in data transmission and documentation as well as for specific applications like identification of postal(zip) codes."
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