PRE2019 3 Group4 State Of The Art Old

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On this separate wiki page the State Of The Art (SOTA) regarding text-to-braille conversion is given.

[1]

Summary: In this paper a low-cost, low-power portable system is described that serves as a braille reading and writing system. It can use a braille keyboard to display the written text in braille, such that visually impaired people can practice their braille reading and writing skill. It also has the capability of converting a text document to braille.

[2]

Summary: The outcomes of a national survey in Greece are presented in this article. In the survey the preferences and choices of students with vision impairments on literacy medium for studying are examined. The study shows that braille and large print are the preferred mediums for studying. However, the majority uses the medium of listening as the best performance medium for studying.

[3]

Summary: This paper describes a system that aims to help the visually impaired by recognizing written text around them and converting it to spoken text, which is played to the user. This system responds to all written text around the user and also notifies the user of the distance to the nearest object. It achieves an accuracy of 84%.

[4]

Summary: This paper describes a way to create braille dots that can be used on a braille display by using an electrothermal design where the dots can be displaced out of the plane by 250 microns and a temperature difference of 58°C with the environment can be achieved. This way, the dots can be small and cheap, requiring only an input voltage of 1.36 V.

[5]

Summary: Electronically refreshable braille displays have been around for some time, but they have been very expensive. This paper presents a low cost refreshable braille display that uses very little power. It also describes open source text to braille scanner using Google’s open source optical character recognition (OCR) engine.

[6]

Summary: In general with the production of text entries or smartphones little attention is paid to people with no or impaired vision. Finding the keys for voice control is also an issue for blind people. The newly introduced BrailleEnter will support non-visual interaction with a touchscreen device, since users can tap the screen to raise Braille dots based on Braille coding. When the screen is gently touched, the Braille dots will not rise.

[7]

Summary: From the visually impaired or blind people, being namely 161 million people, only 3% are able to read, write, or count. This is due to the fact that there is lack of Braille reading material in schools. A solution is proposed, where 3D printed visual representations of books are manufactured to improve the learning potential of the target users.

[8]

Summary: Visually impaired individuals are limited in terms of communication, interaction, and personal autonomy due to the lack of Braille literature linked to economic reasons. A portable device is introduced as a reading system for visually impaired individuals, which is based on segmentation, feature extraction, and machine learning for improved accuracy.

[9]

Summary: With the introduction of touchscreens, the accessibility for blind people decreased significantly. Due to the high demands for mobile phones, it is important to also take into account the accessibility for blind people. Research is done to obtain a new way of implementing Braille text in smartphones. From the data of 5 databases, the research is performed.

[10]

Summary: A novel approach to converting Chinese text to Chinese Braille is proposed. A Braille word segmentation model, based on statistical machine leaning, is trained on a Braille corpus, and also on Chinese word segmentation. This will avoid the establishment of syntactic and semantic information rules. Furthermore a statistical model will learn these rules automatically in the background.

[11]

Summary: Design, Prototype and implementation of a Sign Language (ASL) to Braille Converter as well as an English Language to Braille Converter. The article proposes a simple and affordable device which was experimentally verified to give accurate outputs.

[12]

Summary: A comparison between visually impaired (visually impaired print reader: PR, braille reader: BR) and normally sighted (normal vision: NV) school children was performed based on reading rate and comprehension. BR had the lowest reading rate compared to other groups. The findings suggest that visually impaired students required a longer time to read and understand a text.

[13]

Summary: Although several text to braille converters are available, the cost is a factor which prevents this technology to reach all people of society. Therefore a low cost gesture controlled text to braille converter was developed.

[14]

Summary: A system for converting written text into braille was developed. It uses optical character recognition to translate written text into digitized texts, which are then transferred electronically in a braille haptic device. The overall system reliability is 95.68% and the system can process 1 word in 2 seconds.

[15]

Summary: To aid the blind and visually impaired (BVI), a portable text reading system, called Finger-eye was developed. This system uses a small camera placed on the blind person’s finger to continuously process images using the optical character recognition (OCR) method, which are then translated into a refreshable mechanical braille display.

[16]

Summary: This model can translate different types of text files in English to braille. Both the input and output are in text format. It uses six point cells to display braille characters obtained from converting eglish text. It does this using Matlab.

[17]

Summary: This method describes the translation of many different languages into braille, and saving it on a computer. It uses a table-driven method for this, and it should be relatively easy to adjust the method for a related purpose.

[18]

Summary: This portable device includes a scanner of text, converting algorithm for text-to-braille, braille display and braille keyboard for annotation features.

[19]

Summary:A cursive handwriting recognition system using artificial neural networks is applied. The features of each written character in the input is extracted and passed on to the neural network. This network uses data sets of handwriting from many different people to convert the handwriting to text, making it very accurate.

[20]

Summary: An Optical Character Recognition (OCR) program is developed using the Hidden Markov Model. The input is an image of written english text that is converted to printed text, with said model.

References

  1. Sultana, S., Rahman, A., Chowdhury, F. H., & Zaman, H. U. (2018). A novel Braille pad with dual text-to-Braille and Braille-to-text capabilities with an integrated LCD display. 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2017, 195–200. https://doi.org/10.1109/ICICICT1.2017.8342559
  2. Argyropoulos, V., Padeliadu, S., Avramidis, E., Tsiakali, T., & Nikolaraizi, M. (2019). An investigation of preferences and choices of students with vision impairments on literacy medium for studying. British Journal of Visual Impairment, 37(2), 154–168. https://doi.org/10.1177/0264619619838667
  3. Arakeri, M. P., Keerthana, N. S., Madhura, M., Sankar, A., & Munnavar, T. (2018). Assistive Technology for the Visually Impaired Using Computer Vision. 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018, 1725–1730. https://doi.org/10.1109/ICACCI.2018.8554625
  4. Izhar, U., Albermani, F., Preethichandra, D. M. G., Sul, J., & van Rensburg, P. A. J. (2020). An Electrothermally Actuated MEMS Braille Dot. In Lecture Notes in Civil Engineering (Vol. 37, pp. 985–993). Springer. https://doi.org/10.1007/978-981-13-7603-0_93
  5. Hossain, S., Raied, A. A., Rahman, A., Abdullah, Z. R., Adhikary, D., Khan, A. R., Bhattacharjee, A., Shahnaz, C., & Fattah, S. A. (2019, January 3). Text to Braille Scanner with Ultra Low Cost Refreshable Braille Display. GHTC 2018 - IEEE Global Humanitarian Technology Conference, Proceedings. https://doi.org/10.1109/GHTC.2018.8601552
  6. M. Alnfiai, & S. Sampalli (2017). BrailleEnter: A Touch Screen Braille Text Entry Method for the Blind. Procedia Computer Science, vol. 109, pp. 257-264. https://doi.org/10.1016/j.procs.2017.05.349
  7. L. A. D. Arbes, J. M. J. Baybay, J. E. E. Turingan, & M. J. C. Samonte (2019). Tagalog text-to-braille translator tactile story board with 3D printing. IOP Conference Series: Materials Science and Engineering, vol. 482. https://doi.org/10.1088/1757-899X/482/1/012023
  8. G. B. Holanda et al. (2018). Development of OCR system on android platforms to aid reading with a refreshable braille display in real time. Measurement: Journal of the International Measurement Confederation, vol. 120, pp. 150-168. https://doi.org/10.1016/j.measurement.2018.02.021
  9. J. Siqueira et al. (2016). Braille Text Entry on Smartphones: A Systematic Review of the Literature. Proceeding - International Computer Software and Applications Conference, vol. 2, pp. 521-526. https://doi.org/10.1109/COMPSAC.2016.74
  10. X. Wang, Y. Yang, J. Zhang, W. Yiang, H. Liu, & Y. Qian (2017). Chinese to Braille translation based on Braille word segmentation using statistical model. Journal of Shanghai Jiaotong University (Science), vol. 22, pp. 82-86. https://doi.org/10.1007/s12204-017-1804-x
  11. A. Dasgupta, D. Seth, A. Gosh, & A. Nath (2017). Real time sign language to Braille interfacing system. 2017 7th International Conference on Communication Systems and Network Technologies (CSNT), pp. 371-375. https://doi.org/10.1109/CSNT.2017.8418569
  12. Z. Mohammed, & R. Omar (2011). Comparison of reading performance between visually impaired and normally sighted students in Malaysia. British Journal of Visual Impairment, 29(3), pp. 196–207. https://doi.org/10.1177/0264619611415004
  13. V. Kartha, D. S. Nair, S.S. Pranoy P. Pranoy, & P. Jayaprakash (2012). DRISHTI—A gesture controlled text to braille converter. 2012 Annual IEEE India Conference (INDICON), pp. 335-339. https://doi.org/10.1109/INDCON.2012.6420639
  14. Cruz, J.L., Ebreo, J.A., Inovejas, R.A., Medrano, A.R., & Bandala, A. (2017). Development of a text to braille interpreter for printed documents through optical image processing. 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). https://doi.org/10.1109/hnicem.2017.8269523
  15. Z. Liu, Y. Luo, J. Cordero, N. Zhao, & Y. Shen (2016). Finger-eye: A wearable text reading assistive system for the blind and visually impaired. 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), Angkor Wat, pp. 123-128. https://doi.org/10.1109/RCAR.2016.7784012
  16. Sibila R., Senthamil Selvan K., Sowmya B. (2018). TEXT TO BRAILLE CONVERSION USING MATLAB. https://pdfs.semanticscholar.org/fbff/f0210e697ca832c246fba970fe217a22096d.pdf?_ga=2.240427651.1606621666.1581253188-1788626333.1573814345
  17. Blenkhorn P. (1997). A System for Converting Print into Braille. https://pdfs.semanticscholar.org/7f3b/831257f7080ee227ccf1bcd131fca4fc655d.pdf?_ga=2.5744435.1606621666.1581253188-1788626333.1573814345
  18. Kahn S. (2003). PORTABLE BRAILLE COMPUTER DEVICE. https://patentimages.storage.googleapis.com/59/7d/1a/bc096ae158c7ac/US6542623.pdf
  19. Utkarsh Dwivedi1, Pranjal Rajput, Manish Kumar Sharma (2017). Cursive Handwriting Recognition System Using Feature Extraction and Artificial Neural Network. https://pdfs.semanticscholar.org/8292/26f8c745645802b7d76ef3587b1c389cc173.pdf?_ga=2.239371011.1606621666.1581253188-1788626333.1573814345
  20. Aparna A., Muthumani I. (2014). Optical Character Recognition for HandwrittenCursive English characters. https://pdfs.semanticscholar.org/d9fe/fbe2fcaab6580869b4923c66c1a6fa84f950.pdf?_ga=2.235794049.1606621666.1581253188-1788626333.1573814345
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