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.

$ python scripts/formula_recognition.py --config configs/formula_recognition.yaml

Model Configuration#

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.

Formula Recognition Algorithm

Contents