Automated Multilingual Content Delivery for the Visually Impaired via AI-Driven Document Parsing

In multilingual societies, the challenge of accessing printed information remains acute for visually impaired individuals and non-native English speakers. This work introduces a sophisticated and extensible framework that unites advanced Optical Character Recognition (OCR) algorithms and neural machine translation, specifically engineered for English-to-Tamil conversion. In order to elevate recognition reliability, the methodology incorporates enhanced preprocessing techniques including adaptive thresholding and robust noise reduction algorithms, critically improving performance across diverse document types. Architecturally, the system integrates recent advances in character recognition, leveraging the open-source Tesseract engine and contemporary sequence-to-sequence translation models, with modularity for expansion to additional languages and features. Experimental validation utilizes a diverse array of datasets and is substantiated by standard metrics such as BLEU for translation fidelity. Comparative analyses with recent commercial and academic benchmarks are provided, and results are presented with comprehensive statistical analysis. The entire pipeline is implemented for accessible deployment, emphasizing usability, performance, and extensibility. The findings confirm highly competitive OCR accuracy and translation reliability, with particular suitability for resource-constrained environments and social inclusion efforts.

• Publication year

  2025

• Venue

  2025 Fourth International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)

• Publication date

  2025-11-20

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICSTSN67075.2025.11398048
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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