Handwritten Character Recognition Using Neural Networks
DOI:
https://doi.org/10.15662/IJEETR.2026.0802008Keywords:
Face Recognition, Deep Learning, Convolutional Neural Network (CNN), Artificial Intelligence, OpenCV, TensorFlow, Flask, Criminal Identification, Law Enforcement, Digital PolicingAbstract
In the c formation and automation, efficient ai.d accurate text recognition plays a crucial role in bridging the gap between human writing and comp riting and compu, interpretation. Our project, the Handwritten
Character Recognition (HCR) System using leural Networks, addresses this need by establishing an intelligent model capable of identifying and classifying handwi racter with high precision. The system utilizes deep learning techniques, specifically Convolutional Neurai Networks (CNNs), to analyze handwritten input images and cotvert them into machine-readable text.
Built on a scalab t neural network architecture, this system employs advanced prepro- cessing methods su nage normalization, noise reduction, and segmentation to ensure accurate recognition. The crained using a large set of handwritien samples, enabling the model to learn diverse writing styles and patterns. The The trained model then predicts the corresponding characters with improved accuracy through multiple feature extre tion and classification layers.
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