Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{195472,
        author = {p sai pallavi and panja keerthi and n mamatha},
        title = {Smart Image-to-Editable Document Converter Using OCR and Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1473-1478},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195472},
        abstract = {In today’s fast-moving digital environment, a large amount of information is still stored in the form of images such as scanned documents, photographs, and handwritten notes. Converting this information into editable text manually takes a lot of time and effort, and it often leads to errors. To overcome this challenge, this paper presents an intelligent Image-to-Editable Document Converter that automatically extracts text from images and transforms it into structured, editable formats.
The proposed system combines image processing techniques with Optical Character Recognition (OCR) to improve the clarity of input images and accurately recognize textual content. It applies preprocessing steps such as noise removal, image enhancement, and alignment correction to ensure better text detection. The extracted content is then organized into a well-structured document while maintaining basic formatting like paragraphs and spacing.
This approach not only reduces manual work but also improves efficiency and accuracy in document digitization. The system can be useful in various fields including education, business, and record management, where quick and reliable conversion of image-based documents into editable formats is essential.},
        keywords = {OCR, Image Processing, Document Digitization, Text Detection, Layout Analysis, Deep Learning, Document Reconstruction},
        month = {April},
        }