
# Model Card for Model ID

This CNN fine-tuned model is designed to identify Alphabet characters written by children.



## 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,


- **License:** [MIT]
- **Finetuned from model [optional]:** [shahad-alh/arabichar-finetuned-v1]


## Uses

Could be used by anyone who is interested in buliding App, Model, anything that should identify Children's characters handwriting.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.


## Evaluation

training-set accuracy = 80%

Validation-set accuracy = 72%

Test-set Accuuracy = 69%

this is just draft - I'm still working on the training-

## 🚀 Demo

Try it out live here:  
[![Gradio demo](https://img.shields.io/badge/Gradio-Demo-blue)](https://huggingface.co/spaces/your-username/your-space-name)


## Model Card Contact

[More Information Needed]
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