This framework was developed as part of my undergraduate final year project at University and allows for the extraction of tabular data from raster images. It uses line information to locate cells, and an algorithm arranges the cells in memory to reconstruct the tabular structure. It then uses the Tesseract OCR engine to extract the text and returns the entire table as JSON data. It achieved 89% cell detection accuracy when extracting prayer times from timetables (see data folder for some examples).
The main drawbacks are as follows:
- Heavily relies on ruling lines. The table must have all column and row separators, and blurry images can cause a drop in line detection
- Table region detection is quite rudimentary: it looks for the largest quadrilateral in the image
- It can only detect one table
- Tesseract needs more fine tuning for better OCR processing, as sometimes text is not recognized properly.
Below is a summary of how the framework works. This structure is reflected in TableExtractor/__init__.py.
This is the recommended way to run this project as the environment is all set up and ready to use. For convenience, Docker images are automatically built and released on Docker Hub.
To run the Docker container locally:
docker pull abdullahibneat/table-extraction
docker run -d -p 5000:5000 abdullahibneat/table-extraction
Then visit http://localhost:5000 and you're ready to go!
When using a cloud provider, you can change the port by setting the PORT environment variable. In Heroku, the port is set automatically so this repository can simply be pushed to the Heroku remote.
An OCR engine is NOT required to run the project, though without one the returned table object will return cell numbers instead of the cell contents.
If you wish to skip the OCR process, remove the tesserocr requirement from requirements.txt and continue reading the "Get started" section.
This project uses tesserocr as the Tesseract wrapper out-of-the-box. Follow the instructions here to set up tesserocr on your system.
Alternatively, use your own OCR implementation by removing the tesserocr requirement from requirements.txt and updating the "Advanced usage" code in main.py with your own implementation.
- Make sure Python 3.7.x is installed.
❗❗❗THIS IS IMPORTANT❗❗❗ Recommended:Set up a Python 3.7 virtual environment- Install the requirements (make sure you have read the "OCR setup" section above before running this command):
pip install -r requirements.txt - Run the
main.pyfile:python3 main.py
A simple Flask API was written to interact with the table extractor. Run the app module with Flask:
FLASK_APP=app flask run
and visit the address (default: http://localhost:5000). Alternatively, send the image as form data (it can have any name) in a POST request to the root endpoint:
curl -F image=@myImage.jpg http://localhost:5000