Optical Character Recognition Based On Local Invariant Features

The Optical Character Reader (OCR) process means the transition from scanned manual or written images to a machine-determined document. The American Standard Code for Information Interchange (ASCII) in cognitive processing uses OCR. The challenge is two primary folds: word segmentation by letters and character recognition. Implement a new approach to include the two functions by Scale-Invariant Transforming Feature (SIFT) descriptors. To compare SIFT descriptors (RootSIFT), devise a new procedure, that offers outstanding results without increasing computation or storage requirements. In order to identify English characters, Artificial Bee Colony (ABC) method suggests that the back propagation neural network for classification of character be utilized. Conducted experiments with more than 10 measures intended for every character and tested the accuracy for numerical numbers, chart letters, small letters and alphanumeric characters. The performance analysis of ABC optimized neural network algorithm has achieved a maximum accuracy of 97.3077% compared with precision recall and f-measure.