Recognition of Marathi Handwritten Numerals Using Multi-Layer Feed-Forward Neural Network

Marathi is one of the ancient Indian languages majorly spoken in the state of Maharashtra. Marathi is one of the Devanagari script and the literals and numerals are almost similar to Hindi. Recognition of handwritten Marathi numerals is quite challenging task because people have the practice of writing these numerals in variant ways. In this work we have presented a method to recognize the handwritten Marathi numerals using multilayer feed-forward neural network. The scanned document image is pre-processed to eliminate the noise and care is taken to link the broken characters. Each numeral is segmented from the document and it is resized to 7 × 5 pixels using cubic interpolation. While resizing a technique is used to provide better representation for every pixel in segmented numeral. This resized numeral is converted into a vector with 35 values before inputting it to the neural network. We have used 100 sets containing 1000 numerals for this experimentation, of which 50 sets are used for training the network and 50 sets for the testing purpose. The overall recognition rate of the proposed method is 97%.