Handwritten Text Recognition Using Convolutional Neural Network
Atman Mishra A. Sharath Ram Kavyashree C
OCR (Optical Character Recognition) is a technology that offers comprehensive
alphanumeric recognition of handwritten and printed characters at electronic
speed by merely scanning the document. Recently, the understanding of visual
data has been termed Intelligent Character Recognition (ICR). Intelligent
Character Recognition (ICR) is the OCR module that can convert scans of
handwritten or printed characters into ASCII text. ASCII data is the standard
format for data encoding in electronic communication. ASCII assigns standard
numeric values to letters, numeral, symbols, white-spaces and other characters.
In more technical terms, OCR is the process of using an electronic device to
transform 2-Dimensional textual information into machine-encoded text. Anything
that contains text both machine written or handwritten can be scanned either
through a scanner or just simply a picture of the text is enough for the
recognition system to distinguish the text. The goal of this papers is to show
the results of a Convolutional Neural Network model which has been trained on
National Institute of Science and Technology (NIST) dataset containing over a
100,000 images. The network learns from the features extracted from the images
and use it to generate the probability of each class to which the picture
belongs to. We have achieved an accuracy of 90.54% with a loss of 2.53%.