Abstract
Braille recognition is the ability to detect and recognize Braille characters embossed on Braille document. The result is used in several applications such as embossing, printing, translating . . . etc. However, the performance of these applications is affected by poor quality imaging due to several factors such as scanner quality, scan resolution, lighting, and type of embossed documents. In this work, we extend previous research efforts on Braille recognition systems by proposing a new method for Braille image segmentation using Between-Class Variance with Gamma distribution. The technique of Between-Class Variance was proposed by Otsu using a mixture of Gaussian distributions. Gaussian distribution is widely used for modeling the histogram of images, but due to the asymmetric nature of the distribution of gray levels in Braille images, Gamma distribution is more suitable. The proposed method is composed of two main parts. (a) Find the optimal estimated threshold values using Between-Class Variance with a mixture of Gamma distributions. (b) Use the optimal estimated thresholds values to segment Braille images. Our method was applied on several Braille images scanned by flatbed scanner. The experimental results on Braille images using this technique showed improvement in the accuracy of Braille images segmentation.