Brail is a written mode of communication utilized by individuals with visual impairments to engage in interpersonal exchanges. The braille writing system consists of patterns printed on specialized paper that feature embossed dots. Braille documents enable the visually impaired to acquire knowledge and information exclusively through the application of their sense of contact. Comprehending braille is not a simple undertaking, particularly for the general populace. Because braille is not a required subject in Indonesian education, the majority of the population lacks proficiency in the language. This may therefore result in a minor communication barrier between visually impaired individuals and non-impaired individuals. In order to address this challenge, the present study employs digital image processing via the deep convolutional neural network (DCNN) technique to facilitate comprehension of braille document contents by non-braille speakers. This study employs a deep learning technique that is highly accurate, effective at image processing, and capable of recognizing complex patterns. This study employed the following image processing methods: grayscaling, filtering, contrast enhancement, thresholding, morphological operation, and resizing. Following testing in this investigation, it was determined that the proposed method accurately identifies embossed braille images with a precision of 99.63%.

In this paper, a new slant estimation/correction method for Persian handwritten digits and words is presented. Based on the presented method, first using original image and its inversion, a new combined image is constructed. This new image has the same slant as the original image. So its slant is estimated and considered as the slant of original image. Finally, using a shear transformation, the slant of the original image is corrected. The performance of the presented method has been tested using several popular Persian/Arabic databases. Obtained experimental results and comparison with the other methods in the literature confirm that the presented method is accurate, and promising.