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@article{198786,
        author = {A. Chethan and Dwijesh Awasthi and M. Akhil and Md. Abuzar},
        title = {Chalk Capture: An OpenCV based solution for accessible classroom notes},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11596-11604},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198786},
        abstract = {This project proposes an automated classroom lecture note digitization system based on computer vision and document processing techniques. The system captures blackboard images using a camera-based acquisition module and performs image preprocessing operations such as grayscale conversion, noise filtering, thresholding, and perspective correction using OpenCV to enhance text visibility. Optical character recognition is applied to the processed images to extract textual content from chalk-written lecture notes. The recognized text is post-processed and formatted to generate structured PDF documents. These documents are stored on a centralized server and made accessible through a web-based interface, enabling efficient retrieval and remote access. The proposed system aims to improve text recognition accuracy under varying classroom conditions, reduce manual documentation effort, and provide a scalable framework for digital lecture content management.},
        keywords = {Computer Vision, Optical Character Recognition, Blackboard Content Digitization, Image Preprocessing, Lecture Note Capture, PDF Generation, Web-Based Learning System, Digital Education},
        month = {April},
        }