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Optical Character Recognition for printed Tamil text using Unicode

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2005年第11期6卷 1297-1305页

作者： SEETHALAKSHMI R. SREERANJANI T.R. BALACHANDAR T. abnikant singh Markandey singh Ritwaj Ratan Sarvesh Kumar Shanmugha Arts Science Technology and Research Academy Thirumalaisamudram Thanjavur Tamil Nadu IndiaShanmugha Arts Science Technology and Research Academy Thirumalaisamudram Thanjavur Tamil Nadu IndiaShanmugha Arts Science Technology and Research Academy Thirumalaisamudram Thanjavur Tamil Nadu IndiaShanmugha Arts Science Technology and Research Academy Thirumalaisamudram Thanjavur Tamil Nadu IndiaShanmugha Arts Science Technology and Research Academy Thirumalaisamudram Thanjavur Tamil Nadu IndiaShanmugha Arts Science Technology and Research Academy Thirumalaisamudram Thanjavur Tamil Nadu IndiaShanmugha Arts Science Technology and Research Academy Thirumalaisamudram Thanjavur Tamil Nadu India

Optical Character Recognition (OCR) refers to the process of converting printed Tamil text documents into software translated Unicode Tamil Text. The printed documents available in the form of books, papers, magazines, etc. are scanned using standard scanners which produce an image of the scanned document. As part of the preprocessing phase the image file is checked for skewing. If the image is skewed, it is corrected by a simple rotation technique in the appropriate direction. Then the image is passed through a noise elimination phase and is binarized. The preprocessed image is segmented using an algorithm which de- composes the scanned text into paragraphs using special space detection technique and then the paragraphs into lines using vertical histograms, and lines into words using horizontal histograms, and words into character image glyphs using horizontal histograms. Each image glyph is comprised of 32×32 pixels. Thus a database of character image glyphs is created out of the segmentation phase. Then all the image glyphs are considered for recognition using Unicode mapping. Each image glyph is passed through various routines which extract the features of the glyph. The various features that are considered for classification are the character height, character width, the number of horizontal lines (long and short), the number of vertical lines (long and short), the hori- zontally oriented curves, the vertically oriented curves, the number of circles, number of slope lines, image centroid and special dots. The glyphs are now set ready for classification based on these features. The extracted features are passed to a Support Vector Machine (SVM) where the characters are classified by Supervised Learning Algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts.

关键词：光学性质识别支撑向量人工神经网络编码系统软件翻译

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