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Optical Character Recognition for Receipt

Sample Results

Input Image Output

References

Title Author Year Github Paper Download Model
Character Region Awareness for Text Detection Clova AI Research, NAVER Corp. 2019 https://github.com/clovaai/CRAFT-pytorch https://arxiv.org/abs/1904.01941 craft_mlt_25k.pth
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis Clova AI Research, NAVER Corp. 2019 https://github.com/clovaai/deep-text-recognition-benchmark https://arxiv.org/abs/1904.01906 TPS-ResNet-BiLSTM-Attn-case-sensitive.pth

Folder structure

.
├─ configs               
|  ├─ craft_config.yaml  
|  └─ star_config.yaml   
├─ data
|  ├─ sample_output.jpg  
|  └─ tes.jpg
├─ notebooks                          
|  ├─ export_onnx_model.ipynb         
|  ├─ inference_default_engine.ipynb  
|  ├─ inference_onnx_engine.ipynb     
|  └─ test_api.ipynb                  
├─ src                                                               
|  ├─ text_detector                                         
|  │  ├─ basenet                                           
|  │  │  ├─ __init__.py                           
|  │  │  └─ vgg16_bn.py                           
|  │  ├─ modules                                              
|  │  │  ├─ __init__.py                           
|  │  │  ├─ craft.py                              
|  │  │  ├─ craft_utils.py                        
|  │  │  ├─ imgproc.py                            
|  │  │  ├─ refinenet.py                          
|  │  │  └─ utils.py                              
|  │  ├─ __init__.py                              
|  │  ├─ infer.py                                 
|  │  └─ load_model.py                            
|  ├─ text_recognizer                                           
|  │  ├─ modules                                              
|  │  │  ├─ dataset.py                            
|  │  │  ├─ feature_extraction.py                 
|  │  │  ├─ model.py                              
|  │  │  ├─ model_utils.py                        
|  │  │  ├─ prediction.py                         
|  │  │  ├─ sequence_modeling.py                  
|  │  │  ├─ transformation.py                     
|  │  │  └─ utils.py                              
|  │  ├─ __init__.py                              
|  │  ├─ infer.py                                 
|  │  └─ load_model.py                            
|  ├─ __init__.py                                 
|  ├─ engine.py                                   
|  └─ model.py                                    
├─ .gitignore
├─ CONTRIBUTING.md
├─ Dockerfile
├─ environment.yaml
├─ LICENSE
├─ main.py
├─ pyproject.toml
├─ README.md
├─ requirements.txt
├─ setup.cfg

Model Preparation

You need to create "models" folder to store this:

  • detector_model = "models/text_detector/craft_mlt_25k.pth"
  • recognizer_model = "models/text_recognizer/TPS-ResNet-BiLSTM-Attn-case-sensitive.pth"

Download all of pretrained models from "References" section

Requirements

You can setup the environment using conda or pip

pip install -r requirements.txt

or

conda env create -f environment.yaml

Container

docker build -t receipt-ocr .
docker run -d --name receipt-ocr-service -p 80:80 receipt-ocr
docker start receipt-ocr-service
docker stop receipt-ocr-service

How to contribute?

Check the docs here

Creator