Paper Details

The increasing use of computers for documentations have lead to a large amount of data in the form of variousunstructured documents which are not arranged in a uniform, understandable and integrated way. The processing requiredfor extracting information is still only in its preliminary stage and the hardly predictable document structure make it veryhard to extract information automatically. This project focuses on extracting structured data from unstructured data usingOCR(Optical Character Recognition) and Neural Network. OCR is a technology which is required to deal with common factsas well as complex designed fonts .It focuses on recognizing characters of a document, that is it does script identificationfrom a variety of unstructured printed or handwritten documents. Neural Network uses trained data, that is, the system willalready be trained for recognizing characters of the input unstructured document using synaptic weights. The techniques offeedforwarding and backpropagation will be used in Neural networks which will match the patterns and add new patterns onrecognition. The Multilayer Perceptron(MLP) which will match the input to the output using previously stored data will bethe model for neural network. The system will be implemented and simulated using Java with Neural Network as the backendfor the optical character recognition process. Such an OCR system with Neural Network at it’s back focuses towardsincreases accuracy by eliminating human errors that would occur if the work had be done manually. It also focuses onextracting data irrespective of the noise and the image processing defects.

Anamika Bhaduri, Deeksha Gulati, Sanvar Inamdar, Mayuri Kachare, “OPTICAL CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORK”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 3, Issue 13, pp. -, January 2016.