Open AccessImage Enhancement of Complex Document Images Using Histogram of Gradient Features
Author(s) -
Akhilesh Jain,
N. Shobha Rani,
Nagabasavanna Chandan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.36.24244
Subject(s) - computer science , artificial intelligence , optical character recognition , histogram , block (permutation group theory) , noise (video) , pattern recognition (psychology) , character (mathematics) , moment (physics) , process (computing) , contrast (vision) , computer vision , image (mathematics) , mathematics , physics , geometry , classical mechanics , operating system
Enhancement of document images is an interesting research challenge in the process of character recognition. It is quite significant to have a document with uniform illumination gradient to achieve higher recognition accuracies through a document processing system like Optical Character Recognition (OCR). Complex document images are one of the varied image categories that are difficult to process compared to other types of images. It is the quality of document that decides the precision of a character recognition system. Hence transforming the complex document images to a uniform illumination gradient is foreseen. In the proposed research, ancient document images of UMIACS Tobacco 800 database are considered for removal of marginal noise. The proposed technique carries out the block wise interpretation of document contents to remove the marginal noise that is present usually at the borders of images. Further, Hu moment’s features are computed for the detection of marginal noise in every block. An empirical analysis is carried out for classification of blocks into noisy or non-noisy and the outcomes produced by algorithm are satisfactory and feasible for subsequent analysis.