This paper presents a deep learning-based framework for converting printed Marathi textbook content into Braille to improve accessibility for visually impaired readers. The proposed system integrates image pre-processing, optical character recognition, character segmentation, convolutional neural network-based classification, Unicode reconstruction, and automated Braille translation into a unified pipeline. Before any character recognition can happen, the scanned pages of Marathi textbooks go through a series of preparation steps. The images are first converted to grayscale, then cleaned up to remove noise, made black and white through Binarization, straightened if they're slightly tilted, and finally sharpened for better clarity. Only after this groundwork is done does the actual character extraction and recognition begin. Once the characters are identified, they're pieced back together into proper Marathi Unicode text. From there, a rule-based system maps each character to its corresponding Braille symbol keeping the process structured and predictable. When tested on a real set of scanned textbook pages, the system performed well, achieving strong character recognition accuracy and producing Braille output that's genuinely reliable for printed educational content. The combination of deep learning with Unicode-aware post-processing turned out to make a meaningful difference in how accurately the Marathi text is read and converted. What makes this work particularly promising is that it's not just accurate it's also scalable. It offers a practical path toward building accessible learning materials, and with some further development, the same approach could be adapted for other Indian languages or applied to documents with more complex layouts down the line.