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Arabichar V3
A CNN model for classifying Arabic handwritten characters (Hijaiyah letters), trained on an Arabic handwritten character dataset with an accuracy of 97.64%.
Downloads 152
Release Time : 10/4/2023
Model Overview
This model is based on a custom CNN architecture, specifically designed for recognizing and classifying Arabic handwritten characters. Primarily used in Arabic handwriting recognition scenarios.
Model Features
High Accuracy
Achieves a classification accuracy of 97.64% on the Arabic handwritten character dataset
Specialized Architecture
CNN architecture optimized for the characteristics of Arabic characters
Lightweight
Inference: The model is relatively small and suitable for deployment
Model Capabilities
Arabic Handwritten Character Recognition
Image Classification
Use Cases
Educational Technology
Arabic Language Learning App
Used to recognize learners' handwritten Arabic letters and provide feedback
Recognition accuracy of 97.64%
Document Digitization
Arabic Handwritten Document Transcription
Converts handwritten Arabic documents into digital text
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