Published Paper Details:

In Deep Learning Hand Writing Recognition got lot of attention. Optical Character Recognition (OCR) and Handwritten Character Recognition (HCR) has specific domain to apply. Disparate techniques have been proposed to for character recognition in handwriting recognition system. Despite the fact, sufficient studies and papers describes the techniques for converting textual content from a paper document into machine readable form. In coming days, character recognition system might serve as a key factor to create a paperless environment by digitizing and processing existing paper documents. To classify an individual handwritten word so that handwritten text can be translated to a digital form. We used two main approaches to accomplish this task: classifying words directly and character segmentation. First, we use Convolutional Neural Network (CNN) with various architectures to train a model that can accurately classify words. Next, we use Long Short Term Memory networks (LSTM) with convolution to construct bounding boxes for each character. We then pass the segmented characters to a CNN for classification, and then reconstruct each word according to the results of classification and segmentation. This paper presents a detailed review in the field of Handwritten Character Recognition