Multilingual Text & Handwritten Digit Recognition and Conversion of Regional languages into Universal Language Using Neural Networks

Abstract

The Character recognition techniques equate an illustrative identity with the image of character. Handwritten human character recognition is a machine s ability to obtain and recognize handwritten information from various sources such as papers, photos, tactile touch devices etc. Recognition of handwriting and computer characters is an evolving field of study and has broad uses in banks, offices and industries. The key objective of this research work is to develop a knowledgeable framework for “Handwritten Character Recognition (HCR) victimization Neural Network” which might effectively acknowledge selected type-format character victimization as the substitute Neural Network approach. Neural method is the best method for controlling images, thus style parts square measure less all around plot as compared to various designs. Neural computers do parallel results. Neural computers square measure run during a manner that s utterly different from traditional operation. Neural computers square measure conditioned (not programmed) in such a way, that how it s given in an explicit beginning state (data input); they either assign the information (input file or computer file) into one amongst the quantity of categories or permit the initial data to evolve to maximize an explicit fascinating property. In this research work, a purely handwritten digit recognition using machine learning model as well as character recognition matlab model is used. A translator using MATLAB to beat the barrier of various languages is designed. The projected style is also used for English, Marathi and Guajarati text to speech conversion into English language. Input is taken in English, Marathi and Gujrati text manually to the interface or image of written text or handwritten text and output can be translated in English Language by facilitating use of Optical Character Recognition (OCR) technique. The projected methodology is also used to produce help to folks that lack the ability of speech or non-native speakers. On the other hand, purely handwritten digit recognition using machine learning algorithms is used to interpret the human handwriting to the second person easily and effectively.