A Histogram-Based Two-Stage Adaptive Character Segmentation for Transcription of Inter-Point Hindi Braille to Text

Abstract

Problem statement: The optical Braille character recognition (OBR) system is in substantial need in order to preserve the Braille documents to make them available in future for the large section of visually impaired people. The recognition and transcribing of the double sided Braille document into its corresponding natural text is indeed a challenging task. This difficulty is due to the overlapping of the front side dots (recto) with that of the back side dots (verso) in the inter-point Braille document. In such settings, the habitual method of template matching to distinguish recto and verso dots is unproductive. Approach: A fresh system for double sided Braille dot recognition is proposed, which employs a two-stage highly efficient and an adaptive technique to differentiate the recto and verso dots from an inter-point Braille expending the horizontal and vertical projection profiles along with distance thresholding for Braille character segmentation. Materials: The efficacy of this segmentation technique is demonstrated on a large dataset consisting of Hindi Devanagari Braille documents with varying image resolution and with diverse word patterns. The primary reason for choosing the Hindi Devanagari Braille is that, Hindi is the national language of India and OBR for the Hindi Devanagari Braille is not available. Results: Braille line segmentation accuracy of 100%, word segmentation accuracy of 99.8% and character segmentation accuracy of 99.4% has been accomplished. Conclusion: This effort of OBR development for Hindi Devanagari Braille has been done for the first time. The proposed method is tolerant to merging of Braille dots and presence of half characters.