Abstract: Handwriting Detection is a technique or ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, touch screen, photographs etc. Handwritten Text recognition is one of area pattern recognition. The purpose of pattern recognition is to categorize or classification data or object of one of the classes or categories. Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused on deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. This system will be applied to detect the writings of different format. The development of handwriting is more sophisticated, which is found various kinds of handwritten character such as digit, numeral, cursive script, symbols, and scripts including English and other languages.

Keywords: CNN, RNN, CTC, Tensor Flow, OCR, SoftMax

| DOI: 10.17148/IARJSET.2021.86148