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# Formula Recognition Module Development Tutorial
## I. Overview
The formula recognition module is a crucial component of OCR (Optical Character Recognition) systems, responsible for converting mathematical formulas in images into editable text or computer-readable formats. The performance of this module directly impacts the accuracy and efficiency of the entire OCR system. The module typically outputs LaTeX or MathML codes of mathematical formulas, which are then passed on to the text understanding module for further processing.
## II. Supported Model List
| Model | Model Download Link |
BLEU Score |
Normed Edit Distance |
ExpRate (%) |
Model Size (M) |
Description |
| PP-FormulaNet-S | Inference Model/Trained Model |
0.8821 |
0.0823 |
40.01 |
89.7 M |
LaTeX-OCR is a formula recognition algorithm based on an autoregressive large model. By adopting Hybrid ViT as the backbone network and transformer as the decoder, it significantly improves the accuracy of formula recognition. |
Note: The above accuracy metrics are measured on the LaTeX-OCR formula recognition test set.
## III. Quick Integration
> β Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
After installing the wheel package, a few lines of code can complete the inference of the formula recognition module. You can switch models under this module freely, and you can also integrate the model inference of the formula recognition module into your project. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png) to your local machine.
```bash
from paddlex import create_model
model = create_model("PP-FormulaNet-S")
output = model.predict("general_formula_rec_001.png", batch_size=1)
for res in output:
res.print(json_format=False)
res.save_to_json("./output/res.json")
```
For more information on using PaddleX's single-model inference API, refer to the [PaddleX Single Model Python Script Usage Instructions](../../instructions/model_python_API.en.md).
## IV. Custom Development
If you aim for higher accuracy with existing models, you can leverage PaddleX's custom development capabilities to develop better formula recognition models. Before developing formula recognition models with PaddleX, ensure you have installed the PaddleOCR-related model training plugins for PaddleX. The installation process can be found in the [PaddleX Local Installation Guide](../../../installation/installation.en.md).
### 4.1 Data Preparation
Before model training, you need to prepare the corresponding dataset for the task module. PaddleX provides a data validation function for each module, and only data that passes the validation can be used for model training. Additionally, PaddleX provides demo datasets for each module, which you can use to complete subsequent development. If you wish to use private datasets for model training, refer to the [LaTeX-OCR Formula Recognition Project](https://github.com/lukas-blecher/LaTeX-OCR).
#### 4.1.1 Demo Data Download
You can download the demo dataset to a specified folder using the following command:
```bash
wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/ocr_rec_latexocr_dataset_example.tar -P ./dataset
tar -xf ./dataset/ocr_rec_latexocr_dataset_example.tar -C ./dataset/
```
#### 4.1.2 Data Validation
A single command can complete data validation:
```bash
python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
```
After executing the above command, PaddleX will validate the dataset and summarize its basic information. If the command runs successfully, it will print `Check dataset passed !` in the log. The validation results file is saved in `./output/check_dataset_result.json`, and related outputs are saved in the `./output/check_dataset` directory in the current directory, including visual examples of sample images and sample distribution histograms.
π Details of Validation Results (Click to Expand)
The specific content of the validation result file is:
{
"done_flag": true,
"check_pass": true,
"attributes": {
"train_samples": 9452,
"train_sample_paths": [
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0109284.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0217434.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0166758.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0022294.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/val_0071799.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0017043.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0026204.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0209202.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/val_0157332.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0232582.png"
],
"val_samples": 1050,
"val_sample_paths": [
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0070221.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0157901.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0085392.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0196480.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0096180.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0136149.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0143310.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0004560.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0115191.png",
"../dataset/ocr_rec_latexocr_dataset_example/images/train_0015323.png"
]
},
"analysis": {
"histogram": "check_dataset/histogram.png"
},
"dataset_path": "./dataset/ocr_rec_latexocr_dataset_example",
"show_type": "image",
"dataset_type": "FormulaRecDataset"
}
In the above validation results, check_pass being True indicates that the dataset format meets the requirements. Explanations for other indicators are as follows:
* attributes.train_samples: The number of training samples in this dataset is 9452;
* attributes.val_samples: The number of validation samples in this dataset is 1050;
* attributes.train_sample_paths: A list of relative paths to the visualized training samples in this dataset;
* attributes.val_sample_paths: A list of relative paths to the visualized validation samples in this dataset;
Additionally, the dataset verification also analyzes the distribution of sample numbers across all categories in the dataset and generates a distribution histogram (histogram.png):
π Details of Format Conversion / Dataset Splitting (Click to Expand)
(1) Dataset Format Conversion
The formula recognition supports converting FormulaRecDataset format datasets to LaTeXOCRDataset format ( PKL format ). The parameters for dataset format conversion can be set by modifying the fields under CheckDataset in the configuration file. Examples of some parameters in the configuration file are as follows:
CheckDataset:convert:enable: Whether to perform dataset format conversion. Formula recognition supports converting FormulaRecDataset format datasets to LaTeXOCRDataset format, default is True;src_dataset_type: If dataset format conversion is performed, the source dataset format needs to be set, default is FormulaRecDataset;
For example, if you want to convert a FormulaRecDataset format dataset to LaTeXOCRDataset format, you need to modify the configuration file as follows:
......
CheckDataset:
......
convert:
enable: True
src_dataset_type: FormulaRecDataset
......
Then execute the command:
python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
After the data conversion is executed, the original annotation files will be renamed to xxx.bak in the original path.
The above parameters also support being set by appending command line arguments:
python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example \
-o CheckDataset.convert.enable=True \
-o CheckDataset.convert.src_dataset_type=FormulaRecDataset
(2) Dataset Splitting
The parameters for dataset splitting can be set by modifying the fields under CheckDataset in the configuration file. Examples of some parameters in the configuration file are as follows:
CheckDataset:split:enable: Whether to re-split the dataset. When set to True, dataset splitting is performed, default is False;train_percent: If the dataset is re-split, the percentage of the training set needs to be set, which is an integer between 0 and 100, and the sum with val_percent should be 100;
For example, if you want to re-split the dataset with 90% for the training set and 10% for the validation set, you need to modify the configuration file as follows:
......
CheckDataset:
......
split:
enable: True
train_percent: 90
val_percent: 10
......
Then execute the command:
python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example
After the data splitting is executed, the original annotation files will be renamed to xxx.bak in the original path.
The above parameters also support being set by appending command line arguments:
python main.py -c paddlex/configs/modules/formula_recognition/PP-FormulaNet-S.yaml \
-o Global.mode=check_dataset \
-o Global.dataset_dir=./dataset/ocr_rec_latexocr_dataset_example \
-o CheckDataset.split.enable=True \
-o CheckDataset.split.train_percent=90 \
-o CheckDataset.split.val_percent=10
π More Details (Click to Expand)
- During model training, PaddleX automatically saves the model weight files, with the default being
output. If you need to specify a save path, you can set it through the -o Global.output field in the configuration file.
- PaddleX shields you from the concepts of dynamic graph weights and static graph weights. During model training, both dynamic and static graph weights are produced, and static graph weights are selected by default for model inference.
-
After completing the model training, all outputs are saved in the specified output directory (default is ./output/), typically including:
-
train_result.json: Training result record file, recording whether the training task was completed normally, as well as the output weight metrics, related file paths, etc.;
train.log: Training log file, recording changes in model metrics and loss during training;config.yaml: Training configuration file, recording the hyperparameter configuration for this training session;.pdparams, .pdema, .pdopt.pdstate, .pdiparams, .pdmodel: Model weight-related files, including network parameters, optimizer, EMA, static graph network parameters, static graph network structure, etc.;
π More Details (Click to Expand)
When evaluating the model, you need to specify the model weights file path. Each configuration file has a default weight save path built-in. If you need to change it, simply set it by appending a command line parameter, such as -o Evaluate.weight_path=./output/best_accuracy/best_accuracy.pdparams.
After completing the model evaluation, an evaluate_result.json file will be produced, which records the evaluation results, specifically, whether the evaluation task was completed successfully and the model's evaluation metrics, including recall1γrecall5γmAPοΌ