Meitei Mayek Digit Recognition
1. INTRODUCTION:
Pattern recognition has become a very interesting topic for researchers during last few decades. Handwriting recognition is very challenging area of pattern recognition with various practical applications. There are many applications of this form of recognition. Like postal code verification, vehicle number plate recognition, bank cheque processing, Assigning ZIP Codes to letter mail, automatic reading of area code and address from the letter, various data form processing etc. MEETEILON is an Indo-Aryan language. It is the main language of the state Manipur in India. Around 25 lakhs people speak MEETEILON language in Manipur. Many researchers are doing efforts to convert human written things into the machine readable format which saves time and money. Handwritten digit recognition is very difficult because it depends on various persons and their writing styles. It’s easy to recognize English digits compare to MEETEI-MAYEK digits because in English most of the digits have straight lines and less shapes compare to other script digits like Gujarati ,Devanagiri. MEETEI-MAYEK digits are having different characteristics. They are having various shapes and it’s really difficult to recognize those shapes. Due to varieties in shapes there are some characters that are confusing and possibilities for misclassification are very high. Neural Networks are widely applied to pattern recognition areas. Neural Networks can be trained and then tested on various handwritten digits. This paper describes feed forward neural network with back propagation learning approach for the handwritten digit recognition. Optical Character Recognition (OCR) is a very well-studied problem in the vast area of pattern recognition. Its origins can be found as early as 1870 when an image transmission system was invented which used an array of photocells to recognize patterns. Until the middle of the 20th century OCR was primarily developed as an aid to the visually handicapped. With the
Pattern recognition has become a very interesting topic for researchers during last few decades. Handwriting recognition is very challenging area of pattern recognition with various practical applications. There are many applications of this form of recognition. Like postal code verification, vehicle number plate recognition, bank cheque processing, Assigning ZIP Codes to letter mail, automatic reading of area code and address from the letter, various data form processing etc. MEETEILON is an Indo-Aryan language. It is the main language of the state Manipur in India. Around 25 lakhs people speak MEETEILON language in Manipur. Many researchers are doing efforts to convert human written things into the machine readable format which saves time and money. Handwritten digit recognition is very difficult because it depends on various persons and their writing styles. It’s easy to recognize English digits compare to MEETEI-MAYEK digits because in English most of the digits have straight lines and less shapes compare to other script digits like Gujarati ,Devanagiri. MEETEI-MAYEK digits are having different characteristics. They are having various shapes and it’s really difficult to recognize those shapes. Due to varieties in shapes there are some characters that are confusing and possibilities for misclassification are very high. Neural Networks are widely applied to pattern recognition areas. Neural Networks can be trained and then tested on various handwritten digits. This paper describes feed forward neural network with back propagation learning approach for the handwritten digit recognition. Optical Character Recognition (OCR) is a very well-studied problem in the vast area of pattern recognition. Its origins can be found as early as 1870 when an image transmission system was invented which used an array of photocells to recognize patterns. Until the middle of the 20th century OCR was primarily developed as an aid to the visually handicapped. With the