A robust probabilistic Braille recognition system

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.