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It offers two approaches for extracting tables, allowing you to choose the one that best suits your needs.\n\n## Installation\n\nYou can easily install **TableCV** using pip:\n\n```bash\npip install tablecv\n```\n\n## Usage\n\n### Approach 1 (using PaddleOCR)\n\n**TableCV** offers a straightforward method to extract tables using PaddleOCR. This approach returns a pandas DataFrame object:\n\n```python\nfrom tablecv import extract_table\n\n# Replace \"image_path\" with the path to your image\nprint(extract_table(image_path=\"your_image.png\"))\n```\n\n### Approach 2 (OCR with Your Preferred Tool)\n\nIf you prefer using a different OCR tool like EasyOCR, KerasOCR, or any other OCR solution, you can still use **TableCV**. First, perform OCR on your image using your chosen tool. The OCR results should be structured as a list of tuples, each containing a bounding box and corresponding text:\n\n```python\n# List of tuples: (bounding box as (x, y, w, h), text)\nocr_results = [\n    ((1, 2, 3, 4), \"a\"),\n    ((4, 5, 6, 7), \"b\"),\n    # Add more tuples as needed\n]\n```\n\nAfter obtaining your OCR results, you can extract tables from them using **TableCV**:\n\n```python\nfrom tablecv import extract_table_from_ocr\n\n# Replace \"ocr_results\" with your OCR results list\nprint(extract_table_from_ocr(ocr_results))\n```\n\nWith these two approaches, **TableCV** provides flexibility for table extraction from images, whether you prefer using PaddleOCR or another OCR tool of your choice.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finquilabee%2Ftablecv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finquilabee%2Ftablecv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finquilabee%2Ftablecv/lists"}