/Arabic-OCR

OCR system for Arabic language that converts images of typed text to machine-encoded text.

Primary LanguagePython

Arabic OCR

  • OCR system for Arabic language that converts images of typed text to machine-encoded text.
  • The system currently supports only letters (29 letters) ا-ى , لا.
  • The system aims to solve a simpler problem of OCR with images that contain only Arabic characters (check the dataset link below to see a sample of the images).

Setup

Install python then run this command:

pip install -r requirements.txt

Run

  1. Put the images in src/test directory
  2. Go to src directory and run the following command 
    python OCR.py
  3. Output folder will be created with:
    • text folder which has text files corresponding to the images.
    • running_time file which has the time taken to process each image.

Pipeline

Pipeline

Dataset

  • Link to dataset of images and the corresponding text: here.
  • We used 1000 images to generate character dataset that we used for training.

Examples

Line Segmentation

Line

Word Segmentation

Word

Character Segmentation

Word Word Word Word

Performance

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

NOTE

We achieved these results when we used only the flatten image as feature.

References

  1. An Efficient, Font Independent Word and Character Segmentation Algorithm for Printed Arabic Text.

  2. A Robust Line Segmentation Algorithm for Arabic Printed Text with Diacritics.

  3. Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter .