{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-microsoft--table-transformer-structure-recognition","slug":"microsoft--table-transformer-structure-recognition","name":"table-transformer-structure-recognition","type":"model","url":"https://huggingface.co/microsoft/table-transformer-structure-recognition","page_url":"https://unfragile.ai/microsoft--table-transformer-structure-recognition","categories":["image-generation"],"tags":["transformers","pytorch","safetensors","table-transformer","object-detection","arxiv:2110.00061","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_0","uri":"capability://image.visual.table.structure.detection.via.object.detection","name":"table-structure-detection-via-object-detection","description":"Detects and localizes table structural elements (cells, rows, columns, headers) within document images using a DETR-based object detection architecture. The model processes image inputs through a transformer encoder-decoder backbone trained on table annotations, outputting bounding box coordinates and class labels for each detected structural component. This enables downstream parsing of table content by identifying the spatial layout before OCR or content extraction.","intents":["I need to automatically identify where table cells, rows, and columns are located in a scanned document image","I want to extract table structure from PDFs or images before running OCR on the content","I need to programmatically map table boundaries so I can align extracted text to the correct cells","I'm building a document processing pipeline and need to detect tables first, then extract their structure"],"best_for":["document processing teams building table extraction pipelines","developers automating invoice/receipt/form parsing from images","data extraction engineers working with scanned documents or PDFs","teams building document understanding systems that need structural awareness"],"limitations":["Requires clear, reasonably well-formatted tables — performance degrades on heavily rotated, skewed, or low-resolution images","No built-in OCR — only detects structure; requires separate text recognition model for content extraction","Single-image inference only — no batch processing optimization built-in; requires external batching logic","Trained on specific table formats; may have reduced accuracy on highly stylized or non-standard table layouts","Outputs only bounding boxes and class labels — does not perform cell content extraction or table-to-structured-data conversion"],"requires":["PyTorch 1.9+ or TensorFlow 2.x compatible environment","Transformers library 4.5.0+","PIL/Pillow for image loading and preprocessing","CUDA 11.0+ (optional but recommended for inference speed)","Input images in standard formats (JPEG, PNG, TIFF)"],"input_types":["image (JPEG, PNG, TIFF, BMP)","numpy array (H×W×3 uint8 format)","PIL Image objects"],"output_types":["structured data (bounding boxes with coordinates [x_min, y_min, x_max, y_max])","class labels (cell, row, column, table, header, etc.)","confidence scores per detection"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_1","uri":"capability://image.visual.multi.class.table.element.classification","name":"multi-class-table-element-classification","description":"Classifies detected table elements into semantic categories (table, header, body cell, row, column, etc.) using the transformer decoder's classification head. Each detected bounding box is assigned a class probability distribution, enabling downstream systems to distinguish structural roles — headers vs. data cells, row separators vs. column separators — which is critical for correct table reconstruction and content mapping.","intents":["I need to distinguish table headers from data cells so I can preserve semantic meaning during extraction","I want to identify row and column boundaries separately to reconstruct the table grid correctly","I need to classify table elements by their structural role for proper data mapping","I'm building a system that needs to understand table hierarchy (header rows vs. body rows)"],"best_for":["data engineers building table-to-CSV or table-to-JSON conversion pipelines","teams needing semantic preservation during table extraction","developers working on document understanding systems with structured output requirements"],"limitations":["Classification is relative to detected bounding boxes — errors in detection propagate to classification","No support for nested or merged cells — treats all cells as atomic units","Class taxonomy is fixed to the training set; cannot add custom table element types without retraining","Confidence scores may be low for ambiguous elements (e.g., cells that could be header or body)"],"requires":["PyTorch 1.9+","Transformers library 4.5.0+","Model weights loaded from HuggingFace Hub or local cache"],"input_types":["image (JPEG, PNG, TIFF)"],"output_types":["class labels (string: 'table', 'header', 'body', 'row', 'column', etc.)","class probabilities (float [0, 1] per class)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_2","uri":"capability://image.visual.end.to.end.table.localization.in.documents","name":"end-to-end-table-localization-in-documents","description":"Localizes entire tables within document images by detecting the outer table boundary and all internal structural elements in a single inference pass. The model outputs a hierarchical set of bounding boxes representing the full table extent plus all cells, rows, and columns, enabling systems to extract and isolate tables from mixed-content documents (documents with text, images, and tables together).","intents":["I need to find and extract all tables from a multi-page document that contains both text and tables","I want to isolate table regions from the rest of the document for separate processing","I need to determine table boundaries and locations for downstream content extraction","I'm building a document parser that needs to handle mixed-content documents intelligently"],"best_for":["document processing platforms handling diverse document types (reports, invoices, forms)","teams building document segmentation and layout analysis systems","developers creating end-to-end document-to-structured-data pipelines"],"limitations":["Assumes tables are visually distinct with clear boundaries — struggles with borderless or minimal-formatting tables","No page-level context — processes single images independently; requires external logic for multi-page document handling","Does not handle table continuation across page breaks","Performance depends on image quality and resolution; low-resolution images may produce incomplete detections"],"requires":["PyTorch 1.9+","Transformers library 4.5.0+","Image preprocessing (resize, normalize) compatible with model input specifications"],"input_types":["image (JPEG, PNG, TIFF)"],"output_types":["bounding boxes (nested hierarchy: table → rows/columns → cells)","class labels per element","confidence scores"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_3","uri":"capability://image.visual.transformer.based.spatial.reasoning.for.table.structure","name":"transformer-based-spatial-reasoning-for-table-structure","description":"Uses a transformer encoder-decoder architecture to reason about spatial relationships between table elements, learning which cells belong to the same row or column through attention mechanisms. The encoder processes image features and the decoder attends to both image features and previously-detected elements, enabling the model to infer structural relationships (e.g., 'these cells are aligned vertically, so they form a column') rather than relying on explicit grid lines or pixel-level alignment.","intents":["I need to extract table structure from images where grid lines are faint, missing, or irregular","I want the model to understand cell alignment and relationships even in borderless tables","I need robust table parsing that works on diverse table styles and formats","I'm working with documents where tables have been scanned at different angles or resolutions"],"best_for":["teams processing diverse, real-world documents with varying table quality","developers building robust document understanding systems","organizations dealing with scanned or photographed documents with imperfect table formatting"],"limitations":["Transformer inference is slower than CNN-only approaches for single images; requires GPU for practical speed","Attention mechanisms require sufficient context — may fail on very small or isolated table fragments","No explicit geometric constraints — may produce spatially inconsistent outputs (e.g., overlapping cells) on adversarial inputs","Requires well-formed table structure in training data; may not generalize to highly irregular or artistic table layouts"],"requires":["PyTorch 1.9+ with CUDA support recommended","Transformers library 4.5.0+","GPU with 4GB+ VRAM for reasonable inference speed (CPU inference possible but slow)"],"input_types":["image (JPEG, PNG, TIFF)"],"output_types":["bounding boxes with implicit spatial relationships","class labels encoding structural roles"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_4","uri":"capability://image.visual.batch.inference.with.variable.image.sizes","name":"batch-inference-with-variable-image-sizes","description":"Supports inference on images of varying sizes through dynamic padding and resizing, allowing developers to process multiple images in a single batch without manual preprocessing. The model handles aspect ratio preservation and padding internally, outputting detections in original image coordinates, which simplifies integration into document processing pipelines that work with diverse image dimensions.","intents":["I need to process a batch of document images with different resolutions and aspect ratios efficiently","I want to avoid manual image resizing and coordinate transformation in my pipeline","I'm building a production system that needs to handle variable-sized inputs from different scanners or cameras","I need to maximize GPU utilization by batching images of different sizes"],"best_for":["production document processing systems handling diverse input sources","teams building scalable document extraction pipelines","developers optimizing inference throughput on GPU clusters"],"limitations":["Padding overhead increases memory usage for batches with highly variable image sizes","Batch size is limited by GPU memory; very large images may require single-image inference","No built-in batching logic in the model itself — requires external framework (PyTorch DataLoader, Hugging Face Pipelines) for practical batching","Coordinate transformation back to original image space requires careful handling to avoid rounding errors"],"requires":["PyTorch 1.9+","Transformers library 4.5.0+","GPU with sufficient VRAM for batch size (typically 4-8 images per 8GB VRAM)","External batching framework (PyTorch DataLoader or Hugging Face Pipelines)"],"input_types":["image batch (variable sizes, JPEG/PNG/TIFF)"],"output_types":["detections per image (bounding boxes in original image coordinates)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_5","uri":"capability://tool.use.integration.pytorch.and.transformers.ecosystem.integration","name":"pytorch-and-transformers-ecosystem-integration","description":"Natively integrates with PyTorch and the Hugging Face Transformers library, enabling seamless loading, inference, and fine-tuning through standard APIs. The model is distributed as a safetensors checkpoint compatible with Transformers' AutoModel classes, allowing developers to load and use the model with minimal boilerplate code and leverage the ecosystem's utilities for quantization, distillation, and deployment.","intents":["I want to load and use this model with minimal code using standard Transformers APIs","I need to fine-tune this model on my own table data without rewriting training loops","I want to quantize or distill this model for faster inference on edge devices","I'm building a system that uses multiple Transformers models and need consistent APIs"],"best_for":["PyTorch developers familiar with Transformers library","teams building multi-model systems using Hugging Face ecosystem","developers needing to fine-tune or customize the model for specific use cases"],"limitations":["Requires PyTorch and Transformers library installation — adds dependencies to projects","Fine-tuning requires GPU and significant computational resources","Safetensors format is not compatible with older PyTorch versions (< 1.9)","No built-in ONNX export — requires manual conversion for non-PyTorch deployment"],"requires":["Python 3.7+","PyTorch 1.9+","Transformers library 4.5.0+","HuggingFace Hub account (optional, for model caching)"],"input_types":["image (JPEG, PNG, TIFF)"],"output_types":["Transformers-compatible outputs (ObjectDetectionOutput with logits, pred_boxes)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_6","uri":"capability://image.visual.inference.on.cpu.and.gpu.with.automatic.device.selection","name":"inference-on-cpu-and-gpu-with-automatic-device-selection","description":"Supports inference on both CPU and GPU with automatic device selection, allowing developers to run the model in resource-constrained environments or scale across heterogeneous hardware. The model can be moved between devices using standard PyTorch APIs, and inference speed scales appropriately with available hardware, enabling deployment on laptops, servers, or cloud instances without code changes.","intents":["I need to run table detection on a laptop or edge device without GPU","I want to deploy this model to cloud instances and automatically use available GPUs","I'm building a system that needs to work on both high-end and low-end hardware","I need to process documents locally for privacy reasons without cloud APIs"],"best_for":["developers building on-device document processing systems","teams with privacy requirements that prevent cloud-based processing","organizations with heterogeneous hardware (mix of CPUs and GPUs)","developers prototyping on laptops before deploying to production"],"limitations":["CPU inference is significantly slower than GPU (10-50x depending on image size and hardware)","Memory usage on CPU is higher than GPU due to lack of optimization","No automatic batching on CPU — single-image inference is practical limit","Inference time on CPU may be prohibitive for real-time applications (seconds per image)"],"requires":["Python 3.7+","PyTorch 1.9+","Transformers library 4.5.0+","For GPU: CUDA 11.0+ and compatible GPU (NVIDIA recommended)"],"input_types":["image (JPEG, PNG, TIFF)"],"output_types":["bounding boxes and class labels (same format regardless of device)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-structure-recognition__cap_7","uri":"capability://tool.use.integration.open.source.model.weights.and.reproducibility","name":"open-source-model-weights-and-reproducibility","description":"Distributed as open-source model weights under the MIT license, enabling full reproducibility, inspection, and modification. Developers can download weights, inspect the architecture, reproduce training results, and fine-tune on custom data without licensing restrictions or vendor lock-in. The model is hosted on Hugging Face Model Hub with full documentation and community support.","intents":["I need to understand how this model works and inspect its architecture","I want to fine-tune this model on my proprietary data without licensing concerns","I need to reproduce the model's training for research or validation purposes","I want to avoid vendor lock-in and ensure long-term availability of the model"],"best_for":["research teams and academics studying table detection","organizations with strict open-source requirements","teams building proprietary systems that cannot use closed-source models","developers needing full control over model behavior and training"],"limitations":["No commercial support or SLA — community-driven support only","No guarantee of long-term maintenance or updates","Fine-tuning requires significant computational resources and expertise","No pre-built deployment solutions — requires custom integration and deployment"],"requires":["Python 3.7+","PyTorch 1.9+","Transformers library 4.5.0+","For fine-tuning: GPU with 8GB+ VRAM and training data"],"input_types":["image (JPEG, PNG, TIFF)"],"output_types":["bounding boxes and class labels"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.x compatible environment","Transformers library 4.5.0+","PIL/Pillow for image loading and preprocessing","CUDA 11.0+ (optional but recommended for inference speed)","Input images in standard formats (JPEG, PNG, TIFF)","PyTorch 1.9+","Model weights loaded from HuggingFace Hub or local cache","Image preprocessing (resize, normalize) compatible with model input specifications","PyTorch 1.9+ with CUDA support recommended","GPU with 4GB+ VRAM for reasonable inference speed (CPU inference possible but slow)"],"failure_modes":["Requires clear, reasonably well-formatted tables — performance degrades on heavily rotated, skewed, or low-resolution images","No built-in OCR — only detects structure; requires separate text recognition model for content extraction","Single-image inference only — no batch processing optimization built-in; requires external batching logic","Trained on specific table formats; may have reduced accuracy on highly stylized or non-standard table layouts","Outputs only bounding boxes and class labels — does not perform cell content extraction or table-to-structured-data conversion","Classification is relative to detected bounding boxes — errors in detection propagate to classification","No support for nested or merged cells — treats all cells as atomic units","Class taxonomy is fixed to the training set; cannot add custom table element types without retraining","Confidence scores may be low for ambiguous elements (e.g., cells that could be header or body)","Assumes tables are visually distinct with clear boundaries — struggles with borderless or minimal-formatting tables","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7412218480812157,"quality":0.41,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:58.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1326815,"model_likes":214}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft--table-transformer-structure-recognition","compare_url":"https://unfragile.ai/compare?artifact=microsoft--table-transformer-structure-recognition"}},"signature":"t3K0z090Ve2Ai8vVJC6jvTsnm839bexAwp0u+2v1MjgNylcAmYGhp3uWGFWhYGrHM6a98OCnptdfsBaTXxEeAw==","signedAt":"2026-06-20T12:00:39.926Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--table-transformer-structure-recognition","artifact":"https://unfragile.ai/microsoft--table-transformer-structure-recognition","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--table-transformer-structure-recognition","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}