HWR-ver4

Model Card for Model ID

This CNN fine-tuned model is designed to identify Alphabet characters written by children for ages between 4-8.

Model Details

Model Description

This model focuses on adapting a pre-trained CNN model. It is built of three convolutional layers with 32 filters, followed by max-pooling and batch normalization. Another set of three convolutional layers with 64 filters extracts deeper features, followed by another pooling and normalization step. The extracted features are passed through two fully connected layers with dropout, and the final softmax layer classifies the characters into 28 categories.

The base model, trained on AHCD, and I've fine-tuned it on Dhad-Hijja Dataset collection,

• Developed by: [shahad-alh]
• Model type: [Image Classification]
• Language(s) (NLP): [ar (Arabic)]
• License: [MIT]

Recommendations

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How to Get Started with the Model

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Training Details

Training Data

The model was trained on the shahad-alh/Hijja-Dhad-Unified-V4 dataset.

• 📚 Language: Arabic
• 🧮 Size: ~74,000 samples
• 📊 Split: 80% train, 10% validation, 10% test
• 📌 Content: The dataset focuses on distinguishing between the correct usage of "ض" (Dhad) and "ظ" (Zaa) in Arabic orthography and phonetics.

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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