MIZO LANGUAGE OCR

About Model

This model is developed to perform Optical Character Recognition (OCR), a computer vision task that automatically converts text appearing in images into machine-readable digital text. The model is trained using a labeled dataset containing 14,000 document samples where each image is paired with its correct text transcription. Additional metadata such as font type, font size, and document category helps capture the diversity of document layouts and visual styles. The primary objective of the model is to recognize printed text accurately from scanned pages, books, and other document images while maintaining robustness against variations in font styles, font sizes, and document quality. By learning the relationship between image features and textual labels, the model can generalize to unseen documents and perform reliable text extraction. The training dataset contains images representing multiple document types, including books and older scanned documents. These variations improve the model's ability to handle different scanning qualities, page layouts, and typography. Each training sample consists of a document image linked to its exact ground-truth text, enabling supervised learning and accurate prediction during inference. Potential applications of this OCR model include: Document digitization and archival Automated text extraction from scanned books Digital library creation Searchable PDF generation Intelligent document processing Educational and research datasets Historical document preservation Data entry automation Business document management The model is intended to reduce manual transcription effort while improving the speed and accuracy of document processing. It can serve as a foundation for downstream Natural Language Processing (NLP) tasks such as document indexing, information retrieval, text summarization, translation, and knowledge extraction. The dataset has been structured to ensure consistency between document images and their corresponding text labels, allowing efficient model training and evaluation. The inclusion of document metadata further supports research into OCR performance across different typography and document characteristics. Overall, this OCR model provides a reliable solution for recognizing printed text from document images and contributes to the automation of document digitization workflows. It is suitable for academic research, industrial document processing, digital archiving projects, and the development of intelligent OCR systems capable of handling a wide range of printed documents.