Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Maria Stella.A | Banupriya.M  unnamed

Volume 03 Issue 07 2016

ISSN no:  2348-3121

Page no: 36-38

Abstract – Camera-based assistive content read system is to help blind persons read content names and item bundling from hand-held questions in their day by day lives. To confine the item from jumbled foundations or other encompassing protests in the cam view. The project work is framed into three stages. First, Image capturing – Using a mini camera ,the text which the person need to read get captured as an image and  have to send to the image processing Platform. Secondly, Text identification – Using text recognition algorithm, the text will get filtered from the image Finally, speech output- the identification text codes are output to blind users in speech by using neural network in MATLAB. 

Keywords— Text Recognition, OCR, Neural Network 


  1. D’Albe,“On a Type-Reading Optophone”, Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, Volume 90, Number 619, July 1914.
  2. Ramanathan.R.etal.,“A Novel Technique for English Font Recognition Using Support Vector Machines”, in Advances in Recent Technologies in Communication and Computing, Kottayam, Kerala,2009, pp. 766 – 769.
  3. LPriit.(2011,November 1). How to extract text from images:a comparison of 10 free OCR tools [online]. Available: http://www.freewaregenius.com/2011/11/01/how-to-extract- text-from-images-a-comparison-of-free-ocr-tools/
  4. Line Eikvil,“Optical Character Recognition”, NorskRegnesentral, Oslo, Norway, Rep. 876, 1993.
  5. Yang Guang, “License Plate Character Recognition Based on Wavelet Kernel LS-SVM”, inComputer Research and Development (ICCRD) 3rd International Conference, Shanghai, 2011, pp. 222 – 226.
  6. M Usman Raza, et al., “Text Extraction Using Artificial Neural Networks”, in Networked Computing and Advanced Information Management (NCM) 7th International Conference,, Gyeongju, North Gyeongsang, 2011, pp. 134 – 137.
  7. Fonseca, J.M., et al., “Optical Character Recognition Using Automatically Generated Fuzzy Classifiers”,in Eighth International Conference on Fuzzy Systems and Knowledge Discovery, Shanghai,2011, pp. 448 – 452.
  8. Kumar, M., et al., “k – Nearest Neighbor Based Offline Handwritten Gurmukhi Character Recognition”, in International Conference on Image Information Processing, Himachal Pradesh, 2011, pp. 1 – 4.
  9. Tin Kam Ho and Nagy, G., “OCR with no shape training”, in Proc. OfPattern Recognition 15th International Conference, Barcelona, 2000, pp.27 – 30 vol.4.
  10. Mori, S., et al., “Historical review of OCR research and development”, in Proc. of the IEEE, 1992, pp.1029 – 1058.
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