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Text Retrieval from Natural and Scanned Images
Author(s) -
Jayshree Ghorpade-Aher,
Sumeet Gajbhar,
Amey Sarode,
Govardhan Gayake,
Piyush Daund
Publication year - 2016
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2016907840
Subject(s) - computer science , natural (archaeology) , information retrieval , artificial intelligence , image retrieval , computer vision , image (mathematics) , geology , paleontology
Digital documents are easy to handle, share and store than hard copy of documents. These made people to prefer digital document over hard copy of documents. Digital documents are nothing but scanned images of a document or natural images of notice boards, traffic signs. Text detection is an important process required to extract text from images. Text from images can be extracted using Optical Character Recognition (OCR). OCR works in three phases as preprocessing, segmentation, character recognition. Preprocessing is the first phase which uses different techniques for making text easy to extract from images. In segmentation phase, each character is isolated. Then this will be given as input to OCR recognition phase which will compare it with training data-set and will recognize character. In this survey paper, different techniques for OCR are discussed.

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