Handwritten Character Recognition Using TensorFlow and CNN
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Abstract
Handwritten character recognition is critical for transforming physical documents into digital formats, improving efficiency in document digitization, postal services, and educational fields. This project presents a Handwritten Character Recognition System built using TensorFlow and Convolutional Neural Networks (CNN) to accurately interpret handwritten letters and digits.
The system is trained on a large dataset of handwritten characters, enabling it to recognize patterns and features across various handwriting styles. By incorporating data augmentation, the model is made robust enough to handle real-world variations. The result is a high-accuracy character classification system, which automates tasks such as digitizing handwritten notes and processing forms.
This project highlights the power of deep learning, particularly CNNs, in addressing practical challenges, with potential applications in streamlining workflows and reducing manual effort across industries.
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