OpenCV
4.10.0
Open Source Computer Vision
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Prev Tutorial: Custom deep learning layers support
Next Tutorial: High Level API: TextDetectionModel and TextRecognitionModel
Original author | Zihao Mu |
Compatibility | OpenCV >= 4.3 |
In this tutorial, we first introduce how to obtain the custom OCR model, then how to transform your own OCR models so that they can be run correctly by the opencv_dnn module. and finally we will provide some pre-trained models.
This repository is a good start point for training your own OCR model. In repository, the MJSynth+SynthText was set as training set by default. In addition, you can configure the model structure and data set you want.
After completing the model training, please use transform_to_onnx.py to convert the model into onnx format.
The Python version example code can be found at here.
Example:
Some pre-trained models can be found at https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr?usp=sharing.
Their performance at different text recognition datasets is shown in the table below:
Model name | IIIT5k(%) | SVT(%) | ICDAR03(%) | ICDAR13(%) | ICDAR15(%) | SVTP(%) | CUTE80(%) | average acc (%) | parameter( x10^6 ) |
---|---|---|---|---|---|---|---|---|---|
DenseNet-CTC | 72.267 | 67.39 | 82.81 | 80 | 48.38 | 49.45 | 42.50 | 63.26 | 0.24 |
DenseNet-BiLSTM-CTC | 73.76 | 72.33 | 86.15 | 83.15 | 50.67 | 57.984 | 49.826 | 67.69 | 3.63 |
VGG-CTC | 75.96 | 75.42 | 85.92 | 83.54 | 54.89 | 57.52 | 50.17 | 69.06 | 5.57 |
CRNN_VGG-BiLSTM-CTC | 82.63 | 82.07 | 92.96 | 88.867 | 66.28 | 71.01 | 62.37 | 78.03 | 8.45 |
ResNet-CTC | 84.00 | 84.08 | 92.39 | 88.96 | 67.74 | 74.73 | 67.60 | 79.93 | 44.28 |
The performance of the text recognition model were tested on OpenCV DNN, and does not include the text detection model.
The input of text recognition model is the output of the text detection model, which causes the performance of text detection to greatly affect the performance of text recognition.
DenseNet_CTC has the smallest parameters and best FPS, and it is suitable for edge devices, which are very sensitive to the cost of calculation. If you have limited computing resources and want to achieve better accuracy, VGG_CTC is a good choice.
CRNN_VGG_BiLSTM_CTC is suitable for scenarios that require high recognition accuracy.