Product Label Reading System for Blind People using Support Vector Machine Algorithm
S. Jeyapriyanga1, S. P. Priyadharshini2, Sangeetha S3
1S.Jeyapriyanga, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2S.P.Priyadharshini, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3Sangeetha S, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 13 September 2019 | Revised Manuscript received on 22 September 2019 | Manuscript Published on 10 October 2019 | PP: 179-186 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F10470886S219/19©BEIESP | DOI: 10.35940/ijeat.F1047.0886S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Theoretical—This paper shows a camera based assistive content perusing of item marks from articles to support outwardly tested individuals. Camera fills in as fundamental wellspring of info. To recognize the items, the client will move the article before camera and this moving item will be identified by Background Subtraction (BGS) Method. Content district will be naturally confined as Region of Interest (ROI). Content is extricated from ROI by consolidating both guideline based and learning based technique. A tale standard based content limitation calculation is utilized by recognizing geometric highlights like pixel esteem, shading force, character size and so forth and furthermore highlights like Gradient size, slope width and stroke width are found out utilizing SVM classifier and a model is worked to separate content and non-content area. This framework is coordinated with OCR (Optical Character Recognition) to extricate content and the separated content is given as a voice yield to the client. The framework is assessed utilizing ICDAR-2011 dataset which comprise of 509 common scene pictures with ground truth.
Keywords: Moving Object Detection, Background Subtraction Method, Text Localization, SVM Classifier, Optical Character Recognition (OCR).
Scope of the Article: Systems and Software Engineering