Table Info Extractor

Table Info Extractor is a Python package designed for extracting tables from images and PDFs using OCR (Optical Character Recognition). It supports automatic rotation detection, image preprocessing, and conversion of extracted tables into pandas DataFrames.

Features

• Extract tables from images and PDFs.
• Automatically detect and correct image orientation.
• Enhance OCR results with preprocessing techniques (grayscale conversion, contrast enhancement, thresholding).
• Convert extracted tables into pandas DataFrames for easy data manipulation and analysis.

Installation

You can install Table Info Extractor via pip:

pip install table_info_extractor

Usage

Image To Table Extraction

from table_info_extractor import image_to_table_extract

# Example: Extract tables from an image
extracted_tables_from_image = image_to_table_extract("path_to_your_image.jpg", auto_rotation=True)
print(extracted_tables_from_image)

PDF To Table Extraction

from table_info_extractor import pdf_to_table_extract

# Example: Extract tables from a PDF
extracted_tables_from_pdf = pdf_to_table_extract("path_to_your_pdf.pdf", page_number=1, auto_rotation=True)
print(extracted_tables_from_pdf)

Function Documentation

image_to_table_extract(image_path, auto_rotation=False)

Extracts tables from an image file.

• image_path (str): Path to the input image file.
• auto_rotation (bool): Whether to automatically correct image rotation.

Returns:

• List of pandas DataFrames, each representing a table extracted from the image.

pdf_to_table_extract(pdf_path, page_number, auto_rotation=False)

Extracts tables from a specific page of a PDF file.

• pdf_path (str): Path to the input PDF file.
• page_number (int): Page number from which to extract tables (1-based index).
• auto_rotation (bool): Whether to automatically correct image rotation.

Returns:

• List of pandas DataFrames, each representing a table extracted from the PDF page.

Contribution

Contributions are welcome! To contribute to Table Info Extractor, please follow these steps:

1. Fork the repository and clone it to your local machine.
2. Create a new branch for your feature or bug fix: git checkout -b feature/my-feature.
3. Make your changes and commit them with a descriptive message.
4. Push your changes to the branch: git push origin feature/my-feature.
5. Submit a pull request detailing your changes and why they should be included.

GitHub Repository

Table Data Extractor on GitHub