> ## Documentation Index > Fetch the complete documentation index at: https://docs.vlm.run/llms.txt > Use this file to discover all available pages before exploring further. # paddlepaddle/pp-doclayoutv3 > Document layout detection with reading order from PaddlePaddle. Document **layout detection only** (PP-DocLayoutV3, an RT-DETR instance-segmentation plus reading-order model). It locates and classifies layout regions and predicts the logical reading order in a single forward pass; it does **not** recognize text. Accepts one `image_url` or a `document_url` PDF; no text-only input, and a text prompt is ignored. Default method: `detect`. Because it predicts multi-point boxes rather than axis-aligned rectangles, it holds up on skewed, curved, and photographed pages. Upstream it is the layout stage of PaddleOCR-VL 1.5 and GLM-OCR, so the natural pipeline is layout first (`pp-doclayoutv3`), then recognition on the regions you care about with an OCR model. ## Choosing between this and the OCR models | You want | Use | | ---------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | Region boxes, labels, and reading order, no text | `paddlepaddle/pp-doclayoutv3` | | Text plus line polygons | [`paddleocr/pp-ocrv6`](/gateway/models/paddleocr-pp-ocrv6) (`ocr`) | | Text laid out as Markdown | [`zai-org/glm-ocr`](/gateway/models/zai-org-glm-ocr), [`rednote-hilab/dots.mocr`](/gateway/models/rednote-hilab-dots-mocr) (`markdown`) | | Layout regions **with** their transcribed text in one call | [`rednote-hilab/dots.mocr`](/gateway/models/rednote-hilab-dots-mocr) (`parse_layout`) | ## Output by method | Method | Payload kind | Medium | `content.object` | | -------- | ------------ | ------------- | ------------------------------------------------------------------------------------------------ | | `detect` | json | Image | `pp_doclayoutv3.detect.regions`, with `bbox_xywh`, optional `poly_xy`, `label`, `order`, `score` | | `detect` | json | Document page | `document.page.blocks`, with the same geometry and a zero-based `index` in place of `order` | `detect` is the only method and the default, so `method` can be omitted. This is a pure layout detector with no OCR path: any other method is a `400`. Text mode emits the payload alone for one image, and one `` / `` block per PDF with `format="json"` on every page; JSON mode wraps it in `data`. A page with no detections returns `"items": []`, never a string. ### Region record | Key | Type | Notes | | ----------- | --------------- | ----------------------------------------------------------------------------------------------------- | | `bbox_xywh` | `[x, y, w, h]` | Normalized 0-1 box, rounded to `precision`. | | `poly_xy` | `[[x, y], ...]` | Normalized 0-1 polygon, present when the model returns one; multi-point for skewed or curved regions. | | `label` | `string` | Layout class (see below). | | `order` | `int` | Predicted reading position, 1-based and contiguous after filtering. | | `score` | `float` | Confidence 0-1, rounded to `precision`. | Coordinates are normalized against the input image or the rasterized page. See [The region record](/gateway/methods#region-record). Layout classes: `abstract`, `algorithm`, `aside_text`, `chart`, `content`, `doc_title`, `figure_title`, `footer`, `footnote`, `formula`, `formula_number`, `header`, `image`, `number`, `paragraph_title`, `reference`, `reference_content`, `seal`, `table`, `text`, `vision_footnote`. ### Method parameters | Key | Method(s) | Description | | ----------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `score_threshold` | `detect` | Drop regions below this confidence (0.0-1.0). Default `0.5`. It also binarizes the instance masks that `poly_xy` is traced from, so lowering it both admits weaker regions and loosens polygons. | ## Request ```python Python [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}} from openai import OpenAI client = OpenAI( base_url="https://gateway.vlm.run/v1/openai", api_key="", ) response = client.chat.completions.create( model="paddlepaddle/pp-doclayoutv3", messages=[ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.receipt/playground/2.jpg" }, }, ], } ], extra_body={"method": "detect", "method_params": {"score_threshold": 0.5}}, ) print(response.choices[0].message.content) ``` ```bash cURL [expandable] theme={"theme":{"light":"github-light","dark":"dark-plus"}} curl https://gateway.vlm.run/v1/openai/chat/completions \ -X POST \ -H "Authorization: Bearer $VLMRUN_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "paddlepaddle/pp-doclayoutv3", "method": "detect", "method_params": {"score_threshold": 0.5}, "messages": [ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.receipt/playground/2.jpg" } } ] } ] }' ``` ## Response A single image returns the region container alone, in reading order: ```json theme={"theme":{"light":"github-light","dark":"dark-plus"}} {"object": "pp_doclayoutv3.detect.regions", "items": [{"bbox_xywh": [0.0332, 0.0138, 0.1719, 0.0331], "poly_xy": [[0.0332, 0.0138], [0.2051, 0.0138], [0.2051, 0.047], [0.0332, 0.047]], "label": "doc_title", "order": 1, "score": 0.9712}, {"bbox_xywh": [0.0332, 0.0829, 0.2598, 0.0331], "label": "text", "order": 2, "score": 0.8934}]} ``` With a `document_url` PDF, one block container per page inside the `` / `` blocks: ```text theme={"theme":{"light":"github-light","dark":"dark-plus"}} {"object": "document.page.blocks", "items": [{"index": 0, "bbox_xywh": [0.0274, 0.0114, 0.1419, 0.0274], "label": "doc_title", "score": 0.9712}]} {"object": "document.page.blocks", "items": [{"index": 0, "bbox_xywh": [0.0274, 0.0114, 0.6177, 0.2167], "label": "table", "score": 0.9302}]} ``` One image entry; `content` is the region container: ```json theme={"theme":{"light":"github-light","dark":"dark-plus"}} { "model": "paddlepaddle/pp-doclayoutv3", "method": "detect", "data": [ { "object": "image", "image_hash": "sha256:...", "image_width": 1024, "image_height": 1448, "content": { "object": "pp_doclayoutv3.detect.regions", "items": [ { "bbox_xywh": [0.0332, 0.0138, 0.1719, 0.0331], "poly_xy": [[0.0332, 0.0138], [0.2051, 0.0138], [0.2051, 0.047], [0.0332, 0.047]], "label": "doc_title", "order": 1, "score": 0.9712 }, { "bbox_xywh": [0.0332, 0.0829, 0.2598, 0.0331], "label": "text", "order": 2, "score": 0.8934 } ] } } ] } ``` For a PDF the entry is a document entry and each page's `content` is that page's `document.page.blocks`. See the [Document OCR guide](/gateway/guides/document-ocr#4-parse-the-response). ## Aliases `paddlepaddle/pp-doclayoutv3` (preferred), `pp-doclayoutv3`, and [`PaddlePaddle/PP-DocLayoutV3_safetensors`](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_safetensors) all resolve to this model.