Mobile Text Reader for People with Low Vision

Authors

  • Teng Ren Sin Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Eileen Su Lee Ming Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Yeong Che Fai Center of Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Ong Jian Fu Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Sim Yang Shane Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v74.4654

Keywords:

Text reader, image stitching, optical character recognition, mobile application, text-to-speech, vision impairment

Abstract

People with low vision have visual acuity less than 6/18 and at least 3/60 in the better eye, with correction. The limited vision requires them to enhance their reading ability using magnifying glass or electronic screen magnifier. However, people with severe low vision have difficulty and suffer fatigue from using such assistive tool. This paper presents the development of a mobile text reader dedicated for people with low vision. The mobile text reader is developed as a mobile application that allows user to capture an image of texts and then translate the texts into audio format. One main contribution of this work compared to typical optical character recognition (OCR) engines or text-to-speech engines is the addition of image stitching feature. The image stitching feature can produce one single image from multiple poorly aligned images, and is integrated into the process of image acquisition. Either single or composite image is subsequently uploaded to a cloud-based OCR engine for robust character recognition. Eventually, a text-to-speech (TTS) synthesizer reproduces the word recognized in a natural-sounding speech. The whole series of computation is implemented as a mobile application to be run from a smartphone, allowing the visual impaired to access text information independently. 

References

World Health Organization. 2007. Vision 2020 Global Initiative for the Elimination of Avoidable Blindness Action Plan 2006-2011, France.

M. Zainal, S. M. Ismail, A. R. Ropilah, H. Elias, G. Arumugam, D. Alias, J. Fathilah, T.O Lim, L. M. Ding, P.P. Goh. 2002. Prevalence of Blindness and Low Vision in Malaysian Population: Results from the National Eye Survey. British Journal of Ophthalmology. 86(9): 951–956.

K. Loh, J. Ogle. 2004. Age Related Visual Impairment in the Elderly. The Medical Journal of Malaysia. 59(4): 562.

Visual Impairment And Blindness Fact Sheet N°282, 24 Dec 2013 Available from: http://www.who.int/mediacentre/factsheets/fs282/en/.

T. H. Margrain. 2000. Helping Blind and Partially Sighted People to Read: the Effectiveness of Low Vision Aids. British Journal of Ophthalmology. 84(8): 919–921.

T. V. Tjahja, A. S. Nugroho, J. Purnama, N. A. Azis, R. Maulidiyatul Hikmah, O. Riandi, B. Prasetyo. 2011. Recursive Text Segmentation For Indonesian Automated Document Reader for people with Visual Impairment. International Conference on Electrical Engineering and Informatics (ICEEI). 1–6.

M. Dorigo, B. Harriehausen-Mühlbauer, I. Stengel, P. S. Dowland. 2011. Survey: Improving Document Accessibility from the Blind and Visually Impaired User’s Point of View. Lecture Notes in Computer Science. 6768(2): 129–135

U. Minoni, M. Bianchi, and V. Trebeschi. 2001. A Handheld Real-Time Text Reader. IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA). 354–359.

M. A. Hersh. 2010. The Design and Evaluation of Assistive Technology Products and Devices Part 1: Design. In: JH Stone, M Blouin, editors. International Encyclopedia of Rehabilitation.

E. Peng, P. Peursum and L. Li, S. Venkatesh. 2010. A Smartphone-Based Obstacle Sensor for the Visually Impaired. Ubiquitous Intelligence and Computing. Springer. 590–604.

J. Leimer. 1962. Design Factors in the Development of an Optical Character Recognition Machine. Information Theory. IRE Transactions. 8(2): 167–171.

C.Y. Chen and R. Klette. 1999. Image Stitching—Comparisons and New Techniques. Computer Analysis of Images and Patterns. Springer.

V. Cani. 2011. Image Stitching for UAV Remote Sensing Application. Master Thesis, Universitat Politècnica de Catalunya, Spain.

R. Szeliski. 2006. Image Alignment and Stitching: A Tutorial. Foundations and Trends. Computer Graphics and Vision. 2(1): 1–104.

J. Shi and C. Tomasi. 1994. Good Features to Track. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '94). 593–600.

J. Chen, L.H. Zou, J. Zhang, L.H. Dou. 2009. The comparison and Application of Corner Detection Algorithms. Journal of Multimedia. 4(6): 435–441.

J. Liu, A. Jakas, A. Al-Obaidi, Y. Liu. 2009. A Comparative Study of Different Corner Detection Methods. IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA). 509–514.

R. Chandratre and V. Chakkarwar. 2014. Image Stitching using Harris Feature Detection and Random Sampling. International Journal of Computer Applications. 89(15): 14–19.

M. Brown and D. G. Lowe. 2007. Automatic Panoramic Image Stitching using Invariant Features. International Journal of Computer Vision. 74(1): 59–73.

V. Rankov, R. J. Locke, R. J. Edens, P. R. Barber, B. Vojnovic. 2005. An Algorithm for Image Stitching and Blending. Biomedical Optics. International Society for Optics and Photonics. 190–199.

V. K. Govindan and A. P. Shivaprasad. 1990. Character Recognition—A Review. Pattern Recognition. 23(7): 671–683.

O. Krejcar. 2012. Smart Implementation of Text Recognition (OCR) for Smart Mobile Devices. In INTELLI, The First International Conference on Intelligent Systems and Applications. 19: 24.

M. Tatham and E. Lewis. 1996. Improving Text-to-Speech Synthesis. Proceedings of Fourth International Conference on Spoken Language. 3: 1856–1859.

F. Holm, S. Pearson. 1998. User Interface Controller for Text-to-Speech Synthesizer. US Patent US5850629 A.

Downloads

Published

2015-05-28

How to Cite

Mobile Text Reader for People with Low Vision. (2015). Jurnal Teknologi (Sciences & Engineering), 74(6). https://doi.org/10.11113/jt.v74.4654