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Unicode OCR System
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Abstract The Project is an optical character recognition application using artificial neural networks. The central objective of this project is demonstrating the capabilities of Artificial Neural Network implementations in recognizing extended sets of optical language symbols. The applications of this technique range from document digitizing and preservation to handwritten text recognition in handheld devices. Purpose The main purpose of the Unicode Optical Character Recognition using Artificial Neural Netwirks is to avoid the classic difficulty of being able to correctly recognize even typed optical language symbols is the complex irregularity among pictorial representations of the same character due to variations in fonts, styles and size. This irregularity undoubtedly widens when one deals with handwritten characters. Scope The conventional programming methods of mapping symbol images into matrices, analyzing pixel and/or vector data and trying to decide which symbol corresponds to which character would yield little or no realistic results. Clearly the needed methodology will be one that can detect ‘proximity' of graphic representations to known symbols and make decisions based on this proximity. To implement such proximity algorithms in the conventional programming one needs to write endless code, one for each type of possible irregularity or deviation from the assumed output either in terms of pixel or vector parameters, clearly not a realistic fare Hardware Requirements :
Software Requirements :
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