AI-BASED DIGITIZATION OF HANDWRITTEN HISTORICAL DOCUMENTS IN REGIONAL LANGUAGES

The preservation of historical documents is essential for maintaining cultural heritage and enabling future research. Many valuable historical records exist only in handwritten form and are often written in regional languages, making their preservation and accessibility challenging. Traditional digitization methods mainly focus on scanning documents as images, which does not allow efficient searching, editing, or analysis of the content. To address these challenges, this study explores the use of Artificial Intelligence (AI) for the digitization of handwritten historical documents in regional languages. The proposed approach uses advanced AI techniques such as Optical Character Recognition (OCR), Deep Learning, and Natural Language Processing (NLP) to automatically recognize and convert handwritten text into machine-readable digital formats. The system is designed to handle variations in handwriting styles, ink quality, and aging effects commonly found in historical manuscripts. Special attention is given to regional language scripts, which often have complex characters and limited digital resources. The AI-based digitization process involves document image preprocessing, handwritten text recognition, language modeling, and digital archiving. By training machine learning models on regional language datasets, the system improves accuracy in recognizing handwritten characters and words. The digitized content can then be stored in structured databases, enabling efficient search, translation, and analysis. This approach not only helps in preserving historical documents but also makes them accessible to researchers, historians, and the general public. The implementation of AI-driven digitization can significantly reduce manual effort, improve accuracy, and ensure long-term preservation of valuable historical records in regional languages.

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