Abstract
As a structured document, Braille is the most common means of reading and study for visually handicapped people. The need for converting Braille documents into a computer-readable format has motivated research into the implementation of Braille recognition systems. The main theme of this research is to propose robust probabilistic approaches to different steps of Braille Recognition. The method is meant to be very general in terms of being independent of those parameters of the Braille document such as skewness, scale, and spacing of the page, lines, and characters. For a given Braille document, a statistical method is proposed for estimating the scaling, spacing, and skewness parameters, whereby the detected dots of the Braille document are modeled using a parameterized probability density function. Skewness, scaling, and line spacing are estimated as a solution of a maximum-likelihood (ML) problem, which is solved using expectation maximization. Based on those parameters, each line of the Braille document is extracted, and each of three rows of individual lines is separated based on the vertical projection of the Braille dots. Finally, a scale-independent automatic document gridding procedure is proposed for dot localization and character detection based on a hidden Markov model.
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Kato Y., Sekitani T., Takamiya M., Doi M., Asaka K., Sakurai T., Someya T.: Sheet-type Braille displays by integrating organic field-effect transistors and polymeric actuators. IEEE Trans. Electron Devices 54(2), 202–209 (2007). doi:10.1109/TED.2006
Takamiya M., Sekitani T., Kato Y., Kawaguchi H., Someya T., Sakurai T.: An organic FET SRAM with back gate to increase static noise margin and its application to Braille sheet display. IEEE J. solid-state circuits 42(1), 93–100 (2007). doi:10.1109/JSSC.2006
Rantala J., Raisamo R., Lylykangas J., Surakka V., Raisamo J., Salminen K., Pakkanen T., Hippula A.: Methods for presenting Braille characters on a mobile device with a touchscreen and tactile feedback. IEEE Trans. Haptics 2(1), 28–39 (2009). doi:10.1109/TOH.2009
Lee J.S., Lucyszyn S.: A micromachined refreshable Braille cell. J. Microelectromech. Syst. 14(4), 673–682 (2005). doi:10.1109/JMEMS.2005
Sribunruangrit N., Marque C.K., Lenay C., Hanneton S., Gapenne O., Vanhoutte C.: Speed-accuracy tradeoff during performance of a tracking task without visual feedback. IEEE Trans. Neural Syst. Rehabil. Eng. 12(1), 131–139 (2004). doi:10.1109/TNSRE.2004
Yobas L., Durand D.M., Skebe G.G., Lisy F.J., Huff M.A.: A novel integrable microvalve for refreshable Braille display system. J. Microelectromech. Syst. 12(3), 252–263 (2003). doi:10.1109/JMEMS.2003
Tanaka M., Miyata K., Chonan S.: A wearable Braille sensor system with a post processing. IEEE/ASME Trans. Mechatron. 12(4), 430–438 (2007). doi:10.1109/TMECH.2007
Blenkhorn P., Evans G.: Automated Braille production from word-processed documents. IEEE Trans. Neural Syst. Rehabil. Eng. 9(1), 81–85 (2001)
Krufka S.E., Barner K.E., Aysal T.C.: Visual to tactile conversion of vector graphics. IEEE Trans. Neural Syst. Rehabil. Eng. 15(2), 310–321 (2007)
Namba, M., Zhang, Z.: Cellular neural network for associative memory and its application to Braille image recognition. Neural Networks, 2006. IJCNN ’06. International Joint Conference on 16–21 July, pp. 2409–2414 (2006)
Franois, G., Calders, P.: The reproduction of Braille originals by means of optical pattern recognition. Proceedings 5th International Workshop on Computer Braille Production, Heverlee (1985)
Dubus, J.P., Benjelloun, M., Devlaminck, V., Wauquier, F., Altmayer, P.: Image processing techniques to perform an autonomous system to translate relief Braille into black-ink. Proceedings IEEE Engineering in Medicine and Biology Society 10th Annual International Conference (1988)
Hentzschel, T.W.: An optical Braille reading system. M.Sc. Dissertation, Faculty of Technology, University of Manchester (1992)
Ritchings, R.T., Antonacopoulos, A., Drakopoulos, D.: Analysis of scanned Braille documents. Proceedings International Association for Pattern Recognition Workshop on Document Analysis Systems (1994)
Antonacopoulos, A., Bridson, D.: A robust Braille recognition system. Proceedings of the IAPR International Workshop on Document Analysis Systems, Italy (2004)
Tai Z., Cheng S., Verma P.: Braille Document Parameters Estimation for Optical Character Recognition. Springer, Berlin (2008)
Al-Saleh A., El-Zaart A., AlSalman A.: Dot Detection of Optical Braille Images for Braille Cells Recognition. Springer, Berlin (2008)
Yoosefi Babadi, M., Nasihatkon, B., Azimifar, Z., Fieguth, P.: Probabilistic estimation of Braille document parameters. IEEE International Conference on Image Processing, 2001–2004 (2009)
Dempster A., Laird N.M., Rubin D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. 39, 1–38 (1977)
Bishop C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)
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Yousefi, M., Famouri, M., Nasihatkon, B. et al. A robust probabilistic Braille recognition system. IJDAR 15, 253–266 (2012). https://doi.org/10.1007/s10032-011-0171-7
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DOI: https://doi.org/10.1007/s10032-011-0171-7