.. _algorithm_layout_detection: ================= Layout Detection Algorithm ================= Introduction ================= Layout detection is a fundamental task in document content extraction, aiming to locate different types of regions on a page, such as images, tables, text, and headings, to facilitate high-quality content extraction. For text and heading regions, OCR models can be used for text recognition, while table regions can be converted using table recognition models. Model Usage ================= Layout detection supports following models： .. raw:: html

Model	Description	Characteristics	Model weight	Config file
DocLayout-YOLO	Improved based on YOLO-v10： 1. Generate diverse pre-training data，enhance generalization ability across multiple document types 2. Model architecture improvement, improve perception ability on scale-varing instances Details in DocLayout-YOLO	Speed:Fast, Accuracy:High	doclayout_yolo_ft.pt	layout_detection.yaml
YOLO-v10	Base YOLO-v10 model	Speed:Fast, Accuracy:Moderate	yolov10l_ft.pt	layout_detection_yolo.yaml
LayoutLMv3	Base LayoutLMv3 model	Speed:Slow, Accuracy:High	layoutlmv3_ft	layout_detection_layoutlmv3.yaml

Once enciroment is setup, you can perform layout detection by executing ``scripts/layout_detection.py`` directly. **Run demo** .. code:: shell $ python scripts/layout_detection.py --config configs/layout_detection.yaml Model Configuration ----------------- **1. DocLayout-YOLO / YOLO-v10** .. code:: yaml inputs: assets/demo/layout_detection outputs: outputs/layout_detection tasks: layout_detection: model: layout_detection_yolo model_config: img_size: 1024 conf_thres: 0.25 iou_thres: 0.45 model_path: path/to/doclayout_yolo_model visualize: True - inputs/outputs: Define the input file path and the directory for visualization output. - tasks: Define the task type, currently only a layout detection task is included. - model: Specify the specific model type, e.g., layout_detection_yolo. - model_config: Define the model configuration. - img_size: Define the image long edge size; the short edge will be scaled proportionally based on the long edge, with the default long edge being 1024. - conf_thres: Define the confidence threshold, detecting only targets above this threshold. - iou_thres: Define the IoU threshold, removing targets with an overlap greater than this threshold. - model_path: Path to the model weights. - visualize: Whether to visualize the model results; visualized results will be saved in the outputs directory. **2. layoutlmv3** .. note:: LayoutLMv3 cannot run directly by default. Please follow the steps below to modify the configuration: 1. **Detectron2 Environment Setup** .. code-block:: bash # For Linux pip install https://wheels-1251341229.cos.ap-shanghai.myqcloud.com/assets/whl/detectron2/detectron2-0.6-cp310-cp310-linux_x86_64.whl # For macOS pip install https://wheels-1251341229.cos.ap-shanghai.myqcloud.com/assets/whl/detectron2/detectron2-0.6-cp310-cp310-macosx_10_9_universal2.whl # For Windows pip install https://wheels-1251341229.cos.ap-shanghai.myqcloud.com/assets/whl/detectron2/detectron2-0.6-cp310-cp310-win_amd64.whl 2. **Enable LayoutLMv3 Registration Code** Uncomment the lines at the following links: - `line 2 `_ - `line 8 `_ .. code-block:: python from pdf_extract_kit.tasks.layout_detection.models.yolo import LayoutDetectionYOLO from pdf_extract_kit.tasks.layout_detection.models.layoutlmv3 import LayoutDetectionLayoutlmv3 from pdf_extract_kit.registry.registry import MODEL_REGISTRY __all__ = [ "LayoutDetectionYOLO", "LayoutDetectionLayoutlmv3", ] .. code:: yaml inputs: assets/demo/layout_detection outputs: outputs/layout_detection tasks: layout_detection: model: layout_detection_layoutlmv3 model_config: model_path: path/to/layoutlmv3_model - inputs/outputs: Define the input file path and the directory for visualization output. - tasks: Define the task type, currently only a layout detection task is included. - model: Specify the specific model type, e.g., layout_detection_layoutlmv3. - model_config: Define the model configuration. - model_path: Path to the model weights. Diverse Input Support ----------------- The layout detection script in PDF-Extract-Kit supports input formats such as a ``single image``, a ``directory containing only image files``, a ``single PDF file``, and a ``directory containing only PDF files``. .. note:: Modify the path to inputs in configs/layout_detection.yaml according to your actual data format: - Single image: path/to/image - Image directory: path/to/images - Single PDF file: path/to/pdf - PDF directory: path/to/pdfs .. note:: When using PDF as input, you need to change ``predict_images`` to ``predict_pdfs`` in ``layout_detection.py``. .. code:: python # for image detection detection_results = model_layout_detection.predict_images(input_data, result_path) Change to: .. code:: python # for pdf detection detection_results = model_layout_detection.predict_pdfs(input_data, result_path) Viewing Visualization Results ----------------- When ``visualize`` is set to ``True`` in the config file, the visualization results will be saved in the ``outputs`` directory. .. note:: Visualization is helpful for analyzing model results, but for large-scale tasks, it is recommended to turn off visualization (set ``visualize`` to ``False`` ) to reduce memory and disk usage.