# **YOLO4TAB - An End-to-End Table Extraction System for printed documents**
## **Introduction**
- YOLO4TAB is an end-to-end table extraction system for printed documents. It is based on the YOLOv9 to solve both table detection and table structure recognition problem. Besides, it also includes a skew correction algorithm to correct the skew of the input document.
- This is an end-to-end system that user can input a document image and get the table structure in HTML/LaTex/CSV format. The system also support some custom border styles and alignment for the table.
## **Installation**
- You can easily install the package by using pip:
```bash
pip install yolo4tab
```
## **Usage**
- You can use the package by running the following command:
```python
from yolo4tab import TableExtraction
table_extraction = TableExtraction(device="cpu")
image_path = "/content/example.png"
outputs = table_extraction.extract_table(
image_source=image_path,
)
for idx, table in enumerate(outputs):
print(f"Table {idx}")
print(table["outputs"]["html"])
print(table["outputs"]["latex"])
print(table["outputs"]["csv"])
```
## **Release Version**
- v0.2.3 (26/6/2024) -> Update output format and device selection
- v0.2.2 (25/6/2024) -> Update output format
- v0.2.1 (23/6/2024) -> Update output format
- v0.2.0 (23/6/2024) -> Public release
- v0.1.1 - v0.1.9 (6/2024) -> Under development (Private release)
- v0.1.0 (2/6/2024) -> Update weights and new baseline model (Private release)
- v0.0.2 (17/5/2024) and v0.0.3 (23/05/2024) -> Update codebase (Private release)
- v0.0.1 (16/5/2024) -> Initial version with full pipeline (training, testing, evaluation) for table extraction on printed documents. (Private release)
## Contributing
- vm7608
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"description": "# **YOLO4TAB - An End-to-End Table Extraction System for printed documents**\n\n## **Introduction**\n\n- YOLO4TAB is an end-to-end table extraction system for printed documents. It is based on the YOLOv9 to solve both table detection and table structure recognition problem. Besides, it also includes a skew correction algorithm to correct the skew of the input document.\n\n- This is an end-to-end system that user can input a document image and get the table structure in HTML/LaTex/CSV format. The system also support some custom border styles and alignment for the table.\n\n## **Installation**\n\n- You can easily install the package by using pip:\n\n```bash\npip install yolo4tab\n```\n\n## **Usage**\n\n- You can use the package by running the following command:\n\n```python\nfrom yolo4tab import TableExtraction\n\ntable_extraction = TableExtraction(device=\"cpu\")\nimage_path = \"/content/example.png\"\n\noutputs = table_extraction.extract_table(\n image_source=image_path,\n)\n\nfor idx, table in enumerate(outputs):\n print(f\"Table {idx}\")\n print(table[\"outputs\"][\"html\"])\n print(table[\"outputs\"][\"latex\"])\n print(table[\"outputs\"][\"csv\"])\n```\n\n## **Release Version**\n\n- v0.2.3 (26/6/2024) -> Update output format and device selection\n\n- v0.2.2 (25/6/2024) -> Update output format\n\n- v0.2.1 (23/6/2024) -> Update output format\n\n- v0.2.0 (23/6/2024) -> Public release\n\n- v0.1.1 - v0.1.9 (6/2024) -> Under development (Private release)\n\n- v0.1.0 (2/6/2024) -> Update weights and new baseline model (Private release)\n\n- v0.0.2 (17/5/2024) and v0.0.3 (23/05/2024) -> Update codebase (Private release)\n\n- v0.0.1 (16/5/2024) -> Initial version with full pipeline (training, testing, evaluation) for table extraction on printed documents. (Private release)\n\n## Contributing\n\n- vm7608\n\n",
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