Optical character recognition involves distinguishing, grouping, and, in specific cases, remedying optical images/designs a computerized picture. Online printed text, disconnected and transcribed reports may be every way focused on for acknowledgment. Various applications, example, postal addresses, handled rapidly. Character acknowledgment depends intensely division, include extraction, groupi...

Abstract We address the problem of offline handwritten diagram recognition. Recently, it has been shown that symbols can be directly recognized with deep learning object detectors. However, detectors are not able to recognize structure. propose Arrow R-CNN, first system for joint symbol and structure recognition in diagrams. R-CNN extends Faster detector an arrow head tail keypoint predictor a ...

In the field of Optical Character Recognition (OCR), zoning is used to extract topological information from patterns. In this paper we propose Zone based features for recognition of the mixer of Handwritten and Printed Kannada Digits. A digit image is divided into 64 zones and pixel density is computed for each zone. This procedure is sequentially repeated for entire zone. Finally 64 features a...