If you’ve ever needed to convert images of typed Arabic text into machine-encoded text, you’re in the right place! This guide gives you a step-by-step approach to setting up and running the Arabic OCR project. Whether you’re a seasoned developer or a newcomer, we’ve got something for everyone.

What You Need to Know

The Arabic OCR system focuses on recognizing Arabic letters (from ا to ى, excluding numbers and special symbols), making it vital for handling Arabic texts effectively. Before diving into the setup, let’s briefly understand the flow of our tasks.

Setting Up the Environment

• First, ensure you have Python installed on your machine.
• Next, install the necessary dependencies by executing the following command in your terminal:

pip install -r requirements.txt

Running the OCR

Now it’s time to process your images!

• Place your images into the srctest directory.
• Navigate to the src directory.
• Execute the OCR by running the following command:

python OCR.py

• Upon completion, an output folder will be created containing:
• A text folder with text files corresponding to each image.
• A running_time file indicating the duration taken for processing each image.

Understanding the Process: An Analogy

Think of the Arabic OCR system as a dedicated librarian in a vast library filled with scrolls (your images). Each scroll contains text that needs to be read and transcribed. The librarian diligently looks only for specific letters – the Arabic alphabet – and skips any irrelevant numbers or symbols. After examining each scroll, she carefully writes down the content in a separate notebook (the output text files) and notes down how long it took her to read each scroll in her time log (running_time file).

Testing the Output

To ensure the output is accurate, make sure you have a folder with truth output files, maintaining the same file names as the predictions. Run the comparison by executing:

python edit.py outputtext truth

Performance Metrics

The system showcases impressive performance with:

• Average accuracy: 95%.
• Average processing time per image: 16 seconds.

Do note that these results were obtained using only the flattened image as a feature.

Troubleshooting Common Issues

• If you encounter issues during installation, ensure you have the right version of Python and that your terminal is correctly pointing to your project directory.
• In case the output files are not generated, check if your images are correctly placed in the srctest directory.
• For any discrepancies in the green metric, confirm that the truth output folder contains the required files with matching names.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

References

• An Efficient, Font Independent Word and Character Segmentation Algorithm for Printed Arabic Text.
• A Robust Line Segmentation Algorithm for Arabic Printed Text with Diacritics.
• Arabic Character Segmentation Using Projection Based Approach with Profiles Amplitude Filter.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

With this handy guide, we hope you feel ready to unleash the power of Arabic OCR in your projects! Happy coding!