• Yashwanthreddy Beeravolu
• Rahul Ch
• Ruchitha Chinthala
• Sriram TK

In India, where we see a multi script environment, majority of the documents may contain text information printed in more than one script/language. For automatic processing of such documents through Optical Character Recognition (OCR), it is necessary to identify different script regions of the document. Script identification is the process of identifying scripts in any multi-script environment so that the recognized scripts can be sent to their corresponding OCR software for recognition purpose. Identifications aims to extract information presented in digital documents namely articles, newspapers, magazines and e-books. This has given rise to many language identification systems. The aim of the project is to propose visual clues based procedure to identify different text portions of a document. It has been observed that the three scripts - Telugu, Hindi and English possess their own distinct features. These distinct features could be used as supporting features in the process of script identification system. To identify the type of the language, we use visual clues or features, like top holes, bottom holes, top max row, bottom max row, bottom up curves, top down curves, vertical lines, slant lines, top and bottom component density, coefficient profile, top horizontal lines, without reading the contents of the document. The classification of a particular script is done using Artificial Neural Networks. Once the features are extracted, we can go ahead and train a neural network using the training data for which we already know the true classes. The inputs are fed into the input layer and get multiplied by interconnection weights as they are passed from the input layer to the first hidden layer. As the processed data leaves the first hidden layer, again it gets multiplied by interconnection weights, then summed and processed by the second hidden layer. Finally the data is multiplied by interconnection weights then processed one last time within the output layer to produce the neural network output.

Optical Character Recognition, Artificial Neural Networks.

"Script Identification In Multilingual Documents Using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.575-581, October-2018, Available :http://www.jetir.org/papers/JETIR1810391.pdf

"Script Identification In Multilingual Documents Using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp575-581, October-2018, Available at : http://www.jetir.org/papers/JETIR1810391.pdf