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Character Recognition

Platform : DOT NET

ABSTRACT This project presents creating the Character Recognition System, in which Creating a Character Matrix and a corresponding Suitable Neural Network Structure is key. In addition, knowledge of how one is deriving the input from a character matrix must first be obtained before may proceed. Afterwards, the Feed Forward Algorithm gives insight into the entire working of a neural network; followed by the Back Propagation Algorithm which comprises training, Calculating Error, and Modifying Weights. We shall consider 5 x 6 matrix grid for input of Telugu character. There exist several different techniques for recognizing characters. One common technique uses back propagation in a neural network and this project will investigate how good neural networks solve the character recognition problem. Character recognition using back propagation can be used to demonstrate how many iterations of learning need to be performed to get a satisfactory result. Optical character recognition, or OCR, is a method of converting a scanned image into text. When a page is scanned, it is typically stored as a bit-mapped file in TIF format. OCR can be a very powerful tool for a law firm. The key is its ability to produce a text version of the scanned documents. Once a text file has been created, it then becomes possible to launch a text search and locate any page with a given word or set of words. Our mini project can be considered as a preliminary work in this direction telugu document identification using OCR. First of all, there is an enormous amount of time that must be expended for OCR. Every hour of that time must be paid for. Secondly, there is no guarantee that a critical page will not be missed. Manually reading all of those pages is a very boring task. With OCR, though, this whole process is simplified and made more accurate. Once the documents have been scanned and processed through the OCR module, there is a text version of every page available. The advantages of the character recognition process are that it can save both time and effort when developing a digital replica of the document. Most character recognition procedures can be visualized as consisting of three steps which use: the pre-processor, feature extractor and recognizer. The following are some of the applications of character recognition. 1. Signature Verification 2. Writer Identification 3. In Examination Assessment as a Mark Sheet Reader, etc... The existing systems are English character recognition, Tamil character recognition etc…. The Proposed method avoids feature extraction as it directly compares the test character with the template. MATLAB® is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation. Using the MATLAB product, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and FORTRAN.

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