.. _algorithm_formula_recognition: ============ Formula Recognition Algorithm ============ Introduction ================= Formula detection involves recognizing the content of a given input formula image and converting it to ``LaTeX`` format. Model Usage ================= With the environment properly configured, you can run the layout detection algorithm script by executing ``scripts/formula_recognition.py``. .. code:: shell $ python scripts/formula_recognition.py --config configs/formula_recognition.yaml Model Configuration ----------------- .. code:: yaml inputs: assets/demo/formula_recognition outputs: outputs/formula_recognition tasks: formula_recognition: model: formula_recognition_unimernet model_config: cfg_path: pdf_extract_kit/configs/unimernet.yaml model_path: models/MFR/unimernet_tiny visualize: False - inputs/outputs: Define the input file path and the directory for LaTeX prediction results, respectively. - tasks: Define the task type, currently only containing a formula recognition task. - model: Define the specific model type: Currently, only the `UniMERNet `_ formula recognition model is provided. - model_config: Define the model configuration. - cfg_path: Path to the UniMERNet configuration file. - model_path: Path to the model weights. - visualize: Whether to visualize the model results. Visualized results will be saved in the outputs directory. Support for Diverse Inputs ----------------- The formula detection script in PDF-Extract-Kit supports ``single formula images`` and ``document images with corresponding formula regions``. Viewing Visualization Results ----------------- When the visualize setting in the config file is set to True, ``LaTeX`` prediction results will be saved in the outputs directory.