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table-detection-and-extraction

Object Detection
TensorBoard
PyTorch
English
ultralytics
v8
ultralyticsplus
yolov8
yolo
vision
table detection
table extraction
table classification
document analysis
unstructured document
unstructured table extraction
structured table extraction
unstructured table detection
structured table detection
Eval Results
Model card Files Metrics Community
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table-detection-and-extraction / README.md
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metadata
tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - object-detection
  - pytorch
  - table detection
  - table extraction
  - table classification
  - document analysis
  - unstructured document
  - unstructured table extraction
  - structured table extraction
  - unstructured table detection
  - structured table detection
library_name: ultralytics
library_version: 8.0.43
inference: false
model-index:
  - name: foduucom/table-detection-and-extraction
    results:
      - task:
          type: object-detection
        metrics:
          - type: precision
            value: 0.96196
            name: [email protected](box)

Sure, here's the combined information:

foduucom/table-detection-and-extraction

Model Card for YOLOv8s Table Detection

Model Summary

The YOLOv8s Table Detection model is an object detection model based on the YOLO (You Only Look Once) framework. It is designed to detect tables, whether they are bordered or borderless, in images. The model has been fine-tuned on a vast dataset and achieved high accuracy in detecting tables and distinguishing between bordered and borderless ones.

Model Details

Model Description

The YOLOv8s Table Detection model serves as a versatile solution for precisely identifying tables within images, whether they exhibit a bordered or borderless design. Notably, this model's capabilities extend beyond mere detection – it plays a crucial role in addressing the complexities of unstructured documents. By employing advanced techniques such as bounding box delineation, the model enables users to isolate tables of interest within the visual content.

What sets this model apart is its synergy with Optical Character Recognition (OCR) technology. This seamless integration empowers the model to not only locate tables but also to extract pertinent data contained within. The bounding box information guides the cropping of tables, which is then coupled with OCR to meticulously extract textual data, streamlining the process of information retrieval from unstructured documents.

We invite you to explore the potential of this model and its data extraction capabilities. For those interested in harnessing its power or seeking further collaboration, we encourage you to reach out to us at [email protected]. Whether you require assistance, customization, or have innovative ideas, our collaborative approach is geared towards addressing your unique challenges. Additionally, you can actively engage with our vibrant community section for valuable insights and collective problem-solving. Your input drives our continuous improvement, as we collectively pave the way towards enhanced data extraction and document analysis.

  • Developed by: FODUU AI
  • Model type: Object Detection
  • Task: Table Detection (Bordered and Borderless)

Furthermore, the YOLOv8s Table Detection model is not limited to table detection alone. It is a versatile tool that contributes to the processing of unstructured documents. By utilizing advanced bounding box techniques, the model empowers users to isolate tables within the document's visual content. What sets this model apart is its seamless integration with Optical Character Recognition (OCR) technology. The combination of bounding box information and OCR allows for precise data extraction from the tables. This comprehensive approach streamlines the process of information retrieval from complex documents.

User collaboration is actively encouraged to enrich the model's capabilities. By contributing table images of different designs and types, users play a pivotal role in enhancing the model's ability to detect a diverse range of tables accurately. Community participation can be facilitated through our platform or by reaching out to us at [email protected]. We value collaborative efforts that drive continuous improvement and innovation in table detection and extraction.

Supported Labels 

['bordered', 'borderless']

Uses

Direct Use

The YOLOv8s Table Detection model can be directly used for detecting tables in images, whether they are bordered or borderless. It is equipped with the ability to distinguish between these two categories.

Downstream Use

The model can also be fine-tuned for specific table detection tasks or integrated into larger applications for furniture recognition, interior design, image-based data extraction, and other related fields.

Out-of-Scope Use

The model is not designed for unrelated object detection tasks or scenarios outside the scope of table detection.

Bias, Risks, and Limitations

The YOLOv8s Table Detection model may have some limitations and biases:

  • Performance may vary based on the quality, diversity, and representativeness of the training data.
  • The model may face challenges in detecting tables with intricate designs or complex arrangements.
  • Accuracy may be affected by variations in lighting conditions, image quality, and resolution.
  • Detection of very small or distant tables might be less accurate.
  • The model's ability to classify bordered and borderless tables may be influenced by variations in design.

Recommendations

Users should be informed about the model's limitations and potential biases. Further testing and validation are advised for specific use cases to evaluate its performance accurately.

How to Get Started with the Model

To begin using the YOLOv8s Table Detection model, follow these steps:

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
  • Load model and perform prediction:
from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('foduucom/table-detection-and-extraction')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()

Training Details

Training Data

The model is trained on a diverse dataset containing images of tables from various sources. The dataset includes examples of both bordered and borderless tables, capturing different designs and styles.

Training Procedure

The training process involves extensive computation and is conducted over multiple epochs. The model's weights are adjusted to minimize detection loss and optimize performance.

Metrics

Model Architecture and Objective

The YOLOv8s architecture employs a modified CSPDarknet53 as its backbone, along with self-attention mechanisms and feature pyramid networks. These components contribute to the model's ability to detect and classify tables accurately, considering variations in size, design, and style.

Compute Infrastructure

Hardware

NVIDIA GeForce RTX 3060 card

Software

The model was trained and fine-tuned using a Jupyter Notebook environment.

Model Card Contact

For inquiries and contributions, please contact us at [email protected].

@ModelCard{
  author    = {Nehul Agrawal and
               Pranjal Singh Thakur},
  title     = {YOLOv8s Table Detection},
  year      = {2023}
}