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@article{195468,
        author = {Modu Vijaya Durga Bhavani and Polavarapu Tej Vardhan and Seela Madhu and Mohammad Abdul Sahil and Dr. Gandi Sathyanarayana},
        title = {Handwritten Digit Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {198-199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195468},
        abstract = {The identification of handwritten numerical digits is a widely studied problem in computer vision due to its importance in automation and data processing systems. This paper presents a deep learning-based solution that utilizes a Convolutional Neural Network (CNN) to accurately classify handwritten digits. The model is developed and tested using the MNIST dataset, which contains a large number of labeled grayscale digit images. Instead of relying on manually engineered features, the proposed approach enables the model to learn relevant patterns directly from the input data. The experimental outcomes reveal that the system achieves a high level of accuracy and demonstrates improved reliability when compared with traditional classification techniques.},
        keywords = {Handwritten Digit Recognition, Deep Learning, CNN, Image Analysis, MNIST},
        month = {April},
        }