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+# Table-Transformer-Detection
+
+## Model Description
+**Table-Transformer-Detection** is a 28.8-million-parameter object detection model from Microsoft Research, fine-tuned specifically for table detection in documents.  
+Built on the DETR (DEtection TRansformer) architecture, it locates and identifies tables within unstructured document images such as PDFs and scanned pages.
+
+Trained on PubTables-1M — a large-scale dataset containing nearly one million fully annotated tables from scientific articles — Table-Transformer-Detection delivers strong performance for document table extraction without requiring task-specific architectural customization.
+
+## Features
+- **Table detection**: accurately locates tables in document images, PDFs, and scanned pages.
+- **DETR-based architecture**: leverages a Transformer encoder-decoder on top of a CNN backbone (ResNet) for end-to-end object detection.
+- **Pre-normalization**: uses the "normalize before" setting, applying LayerNorm before self- and cross-attention for improved training stability.
+- **Lightweight**: at only 28.8M parameters (F32), the model is efficient to deploy and run inference on.
+- **Fine-tunable**: can be further fine-tuned on domain-specific document datasets for improved accuracy.
+
+## Use Cases
+- Automated document processing and digitization pipelines
+- Table extraction from academic papers and research articles
+- Invoice and financial document parsing
+- Legal and regulatory document analysis
+- Healthcare and clinical report table extraction
+- Preprocessing step for downstream table structure recognition
+
+## Inputs and Outputs
+**Input**:
+- Document images (JPEG, PNG, etc.) containing one or more tables.
+
+**Output**:
+- Bounding box predictions with confidence scores for each detected table in the image.
+- Class labels identifying detected objects as tables.
+
+## License
+This repo is licensed under the Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0) license, which allows use, sharing, and modification only for non-commercial purposes with proper attribution. All NPU-related models, runtimes, and code in this project are protected under this non-commercial license and cannot be used in any commercial or revenue-generating applications. Commercial licensing or enterprise usage requires a separate agreement. For inquiries, please contact `dev@nexa.ai`