detr-doc-table-detection
Maintainer: TahaDouaji
45
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
detr-doc-table-detection is a model trained to detect both bordered and borderless tables in documents. It is based on the facebook/detr-resnet-50 model, which is a DETR (DEtection TRansformer) model with a ResNet-50 backbone. DETR is an end-to-end object detection model that uses a Transformer architecture. Similar models include the table-transformer-detection model, which is also a DETR-based model fine-tuned for table detection, and the table-transformer-structure-recognition model, which is fine-tuned for table structure recognition.
Model inputs and outputs
Inputs
- Image data
Outputs
- Bounding boxes and class labels for detected tables
Capabilities
The detr-doc-table-detection model can accurately detect both bordered and borderless tables in document images. This can be useful for applications such as document understanding, table extraction, and data mining from scanned documents.
What can I use it for?
You can use this model for object detection tasks, specifically to detect tables in document images. This could be useful for applications like automated data entry, invoice processing, or creating structured datasets from unstructured documents. The model could be further fine-tuned on domain-specific datasets to improve performance for particular use cases.
Things to try
You could experiment with using this model as part of a pipeline for document understanding, where the table detections are used as input to downstream tasks like table structure recognition or cell-level extraction. Additionally, you could explore ways to combine this model with other computer vision or NLP techniques to create more comprehensive document analysis solutions.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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