Devanagari Handwritten Character Recognition Using Neural Network

摘要

Optical Character Recognition is a framework which can translate the images from manually handwritten or printed structure to machine-editable structure. Devanagari script is utilized in numerous Indian dialects like Hindi, Nepali, Marathi, Sindhi and so on. This script structures the establishment of the language like Hindi which is the national and most generally communicated language in India. In current scenario, there is a tremendous interest of accumulating the data in advanced configuration accessible in paper archives and after that later reusing this data by a search procedure. In this paper, we propose a new strategy for recognition of printed Hindi characters in Devanagari script. In this research, the main focus is given towards the recognition of the individual consonant and vowel which can be later reached out to perceive complex inferred words. In this undertaking the fundamental accentuation is given towards the recognition of the individual consonant and vowel. In this project, different pre-processing operations like features extraction, segmentations and classification methods have been studied and implemented to design a sophisticated OCR system for Hindi. In previous research, the classification K-NN technique have been implemented, but in proposed work, we have used hybrid technique which contains k-NN along with neural networks. Proposed approach provides 97.4% recognition rate as compared to 94.5% for existing techniques, which indicates that the proposed approach is better as compared to the techniques used in existing method.