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Hardcopy Text Recognition and Vocalization for Visually Impaired and Illiterates in Bilingual Language

  • K. Shanmugam
  • B. Vanathi
Chapter
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

The new research idea focuses on automatic assistance to be developed for the visually impaired and illiterates. This work aims at hardcopy text extracted and recognized from images in bilingual languages (Tamil and English). The recognized text is then converted into machine-readable text using optical character recognition (OCR) technique and then compared with the stored character set. The proposed approach is capable of recognizing text in a variety of challenging conditions where traditional character recognition systems fail, notably in the presence of substantial blur, low resolution, low contrast, high image noise, and other distortions. The text will then be processed by Microsoft Speech Application Program Interface (SAPI) speech synthesizer and translated to voice as output in both English and Tamil languages. The framework is on implementing the image capturing technique in the embedded system based on Raspberry Pi board and converting it into voice using Win32 SAPI. This model can recognize and convert a maximum of 20 Tamil characters.

Keywords

Optical character recognition Text to speech Bilingual language Program interface Raspberry Pi 

References

  1. Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505–1515.  https://doi.org/10.1007/s10586-017-0798-3.CrossRefGoogle Scholar
  2. Aparna, H., Subramanian, V., Kasirajan, V., Prakash, G. V., Chakravarthy, V. S., & Madhvanath, S. (2004). Online handwriting recognition for Tamil. In Proceedings of the ninth international workshop on frontiers in handwriting recognition (pp. 438–443).  https://doi.org/10.1109/IWFHR.2004.80.
  3. Aparna, K. G., & Ramakrishnan, A. G. (2002). A complete Tamil optical character recognition system. In Proceedings of the fifth international workshop on document analysis systems (pp. 53–57).Google Scholar
  4. Arulmurugan, R., Sabarmathi, K. R., & Anandakumar, H. (2017). Classification of sentence level sentiment analysis using cloud machine learning techniques. Cluster Computing, 1–11.  https://doi.org/10.1007/s10586-017-1200-1.
  5. Kaladharan, N. (2015). An English text to speech conversion system. International Journal of Advanced Research in Computer Science and Software Engineering, 5(10), 1–5.Google Scholar
  6. Kanimozhi, V. M., & Muthumani, I. (2014). Optical character recognition for English and Tamil script. International Journal of Computer Science and Information Technologies, 5(2), 1008–1010.Google Scholar
  7. Keysers, D., Deselaers, T., Rowley, H. A., Wang, L.-L., & Carbune, V. (2017). Multi-language online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1180–1194.CrossRefGoogle Scholar
  8. Mache, S. R., Baheti, M. R., & Namrata Mahender, C. (2015). Review on text-to-speech synthesizer. International Journal of Advanced Research in Computer and Communication Engineering, 4(8), 54–59.  https://doi.org/10.17148/IJARCCE.2015.4812.CrossRefGoogle Scholar
  9. Philips, N., Sonnadara, D. U. J., & Jayananda, M. K. (2005). Text to speech conversion- Tamil language. International Journal of Engineering Research & Technology, 3(3), 911–915.Google Scholar
  10. Punitharaja, K., & Elango, P. (2016). Tamil handwritten character recognition: Progress and challenges. International Journal of Control Theory and Applications, 9(3), 143–151.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • K. Shanmugam
    • 1
  • B. Vanathi
    • 1
  1. 1.Department of Computer Science and EngineeringValliammai Engineering CollegeChennaiIndia

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