Word Level and Efficient Text Recognition Using Sift

Authors

  • K Buvanalakshmi
  • T Geetha

Keywords:

Personality Recognition, Text Detection, Text Recognition

Abstract

Gratitude of text in ordinary segment images is varying into a distinguished examination space owing to the widespread obtainability of imaging strategies in low-priced client product like portable phones. Detecting text in ordinary pictures, as hostile scans of written pages, faxes and commercial cards, is a crucial stage for variety of laptop dream applications, like treated aid for visually impaired and robotic navigation in urban environments. Retrieving texts in every indoor and outdoor situation delivers discourse clues for a good kind of dream tasks. During this project, we execute two processes like text disco actual and text recognition. In text detection, exploit alteration map is then binaries by median strainer and joint with cranny’s edge map to spot the text stroke edge pixels supported feature extraction. The options extractors are Harris Corner, maximal stable extremely sections (Mser), and dense sampling and histogram of oriented gradients (hog) descriptors. Then tool text recognition. The primary one is coaching a personality recognizer to predict the class of a personality in an image patch. The other is coaching a binary personality category for actual personality class to predict the existence of this class in an image patch. The two systems are suitable with two promising needs related with segment text that are text understanding and text retrieval. In supplementary we tend to extend this idea with word level gratitude with lexicon incomes with correct results. And additionally gratitude text in actual era pictures, videos and portable submission pictures.

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Published

2015-05-30

How to Cite

[1]
K. Buvanalakshmi and T. Geetha, “Word Level and Efficient Text Recognition Using Sift”, Int. J. Comp. Sci. Eng., vol. 3, no. 5, pp. 30–34, May 2015.

Issue

Vol. 3 No. 5 (2015): IJCSE May Edition

Section

Review Article

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