table-transformer-detection
by microsoft
Detect and extract tables from document images
microsoft/table-transformer-detectionmixpeek://document_extractor@v1/microsoft_table_transformer_v1Overview
Table Transformer uses DETR architecture adapted for table detection and structure recognition in document images. It identifies table regions and their internal structure (rows, columns, headers).
On Mixpeek, Table Transformer extracts structured table data from PDFs and scanned documents, enabling queries over tabular content in your document collections.
Architecture
DETR-based architecture with ResNet-18 backbone, fine-tuned on PubTables-1M dataset. Separate models for table detection (locating tables) and table structure recognition (parsing rows/columns).
Mixpeek SDK Integration
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
namespace_id: "my-namespace",
collection_name: "my-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "table_extraction",
version: "v1",
parameters: { model_id: "microsoft/table-transformer-detection" },
},
});Capabilities
- Table region detection in document images
- Table structure recognition (rows, columns, headers)
- Handles complex table layouts
- Works with scanned and digital documents
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| PubTables-1M (detection) | AP50 | 96.9 | Smock et al., 2022 — Table 3 |
| PubTables-1M (structure) | AP50 | 95.3 | Smock et al., 2022 — Table 4 |
Performance
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Specification
Research Paper
PubTables-1M: Towards comprehensive table extraction from unstructured documents
arxiv.orgBuild a pipeline with table-transformer-detection
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Open Studio