Transforming Unstructured Data into Accessible Braille Content Using AI

The conversion of unformatted electronic data into the useable formats is crucial in making sure that visually impaired people have equal opportunities to access. The paper is a proposal of an AI-based tool that will transform unstructured data into standardized Braille material in an efficient and accurate way. The proposed solution combines Optical Character Recognition, Natural Language Processing and Machine Learning and extracts, cleanses and analyzes text on scanned documents, images, PDF files, and web pages. Deep learning models also increase the quality of Braille translation because, in addition to the character recognition, it also increases the contextual understanding. The system will do automated text extraction, noise reduction, language detection and semantic processing and encode the material in Braille that can be displayed digitally and embossed or cut into digital displays and embossing machines. The experimental results prove that the solution can serve real-time processing and decrease the amount of effort required to conduct manual transcription considerably. The suggested system helps enhance the availability of information and introduce inclusive online communication.

@article{ijter2026212606154791,
  author = {G. Parvathi Devi and G. Harika and M. Sravani and  S. Dhaneesha},
  title = {Transforming Unstructured Data into Accessible Braille Content Using AI},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2026},
  volume = {2},
  number = {6},
  pages = {114-120},
  doi =  {10.64823/ijter.2606010},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212606154791/transforming-unstructured-data-into-accessible-braille-content-using-ai},
  abstract = {The conversion of unformatted electronic data into the useable formats is crucial in making sure that visually impaired people have equal opportunities to access. The paper is a proposal of an AI-based tool that will transform unstructured data into standardized Braille material in an efficient and accurate way. The proposed solution combines Optical Character Recognition, Natural Language Processing and Machine Learning and extracts, cleanses and analyzes text on scanned documents, images, PDF files, and web pages. Deep learning models also increase the quality of Braille translation because, in addition to the character recognition, it also increases the contextual understanding. The system will do automated text extraction, noise reduction, language detection and semantic processing and encode the material in Braille that can be displayed digitally and embossed or cut into digital displays and embossing machines. The experimental results prove that the solution can serve real-time processing and decrease the amount of effort required to conduct manual transcription considerably. The suggested system helps enhance the availability of information and introduce inclusive online communication.},
  keywords = {Braille Accessibility • Artificial Intelligence • Unstructured Data • NLP • Assistive Technology • OCR},
  month = {Jun},
}

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