PP-DocLayoutV3
by PaddlePaddle
High-accuracy document layout analysis with instance segmentation
PaddlePaddle/PP-DocLayoutV3mixpeek://document_extractor@v1/paddle_pp_doclayoutv3_v1Overview
PP-DocLayoutV3 is PaddlePaddle's third-generation document layout analysis model that combines object detection with instance segmentation for precise document structure understanding. Built on an efficient backbone with 33.3M parameters, it identifies and segments 23 document element types including text blocks, tables, figures, headers, footers, and mathematical formulas. The model uses a multi-scale feature pyramid network for handling elements of varying sizes.
Architecture
Detection + instance segmentation architecture built on PaddleDetection. Uses an FPN backbone for multi-scale feature extraction, with separate detection and segmentation heads. The model predicts bounding boxes and pixel-level masks for 23 document element categories simultaneously, enabling precise layout parsing even with overlapping or nested elements.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so PP-DocLayoutV3 runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
"https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
{
method: "POST",
headers: {
Authorization: "Bearer API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
collection_id: "col_your_collection",
documents: [
{
document_id: "asset-00412",
// Boxes, masks, depth maps and anomaly scores are structured
// results, not vectors. They go in payload and are reachable
// through pre_filters on a retriever, not through similarity.
payload: {
detections: modelOutput,
source_key: "archive/2026/asset-00412",
},
},
],
}),
},
);
// No managed alternative for an open label set. Two extractors do emit a
// bbox, for the one thing each detects: document_graph_extractor@v1 per
// layout block, face_identity_extractor@v1 per face. Nothing ships that
// returns masks, depth maps or anomaly scores.Capabilities
- Document layout detection
- Instance segmentation of page elements
- Table region detection
- Figure and caption extraction
- Mathematical formula localization
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| PubLayNet | [email protected] | 96.2 | Model card |
| DocLayNet | [email protected] | 79.8 | Model card |
| CDLA | [email protected] | 90.1 | Model card |
Performance
Common Pipeline Companions
Explore on Mixpeek
Compare alternatives in this category
Hand-picked tools & platforms compared
Deep-dive technical guide
See how Mixpeek runs models as extractors
Store & search embeddings at scale
Usage-based pricing for pipelines
Compare models, APIs & infrastructure
Specification
Research Paper
Model paper or technical report
arxiv.orgBuild a pipeline with PP-DocLayoutV3
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Run it on your own data, free