Arabic-Handwritten-Images-Recognition

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Arabic-Handwritten-Images-Recognition

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A deep learning model to classify the Arabic letters and digits easily.

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- model.h5
- model.yaml
- weights.hdf5
Arabic_Handwritten_Recognition.ipynb
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LICENSE
README.md

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# Arabic Handwritten Recognition

The automatic recognition of text on scanned images has enabled many applications
such as searching for words in large volumes of documents, automatic sorting of
postal mail, and convenient editing of previously printed documents. The domain of
handwriting in the Arabic script presents unique technical challenges and has been
addressed more recently than other domains. Many different methods have been
proposed and applied to various types of images.
Here we will focus on the recognition part of handwritten Arabic letters and digits
recognition that face several challenges, including the unlimited variation in human
handwriting and the large public databases.

In this project we built a model which can classify a new image to an arabic
letter or digit and get accuracy of 98.86% when testing on more than 13000 different images.

## Dataset
You can download the dataset from [here](https://drive.google.com/open?id=1F-P6uupjVPHgHZjd34O1X8EFn-Xp-SiD)

## Prerequisites
- You need to install Jupyter Notebook or use [Colab](https://colab.research.google.com/) to run the iPython Noteboob direclty.
- You should also have python worked at version 2.6 or higher.
- You should install Keras (follow the documentation [here](https://keras.io/)).

## Authors
[AmrHendy](https://github.com/AmrHendy)

## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details

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