ALPR dataset, software investigation

1. Datasets

Some datasets can be used to train Automatic License Plate Recognition (ALPR).

• Caltech Cars
• English License Plate
• UCSD-Stills (UCSD car dataset)
• UFPR-ALPR
• CCPD: Chinese City Parking Dataset
• Holistic Recognition of Low Quality License Plates by CNN using Track Annotated Data
• OpenALPR benchmark
• 500 Images of the Rear View

A quick glances at those datasets is :

In a short word, the following datasets are large enough to be used in building ALPR

• UFPR-ALPR : For US license plate
• CCPD: Chinese City Parking Dataset : For chinese license plate

There’re other sites that can we use to mined license plate images such as:

• Olav’s License Plate Pictures
• Plates Mania : This is the recommeded
• License Plate Portal

2. Softwares

The following open-source softwares are available to use in commercial ALPR:

• ultimateALPR-SDK : Run very fast even for embedded devices(105fps on GTX 1070, 47fps on Snapdragon 855, 12fps on Raspberry Pi 4) . The source code can run locally. The price is at pricing. They also has the web demo at webapp
• openalpr : The most famous ALPR software. It was built on opencv and tesseract, so when comparing to deep learning approach it is not that strong. The license is AGPL-3.0 so it’s quite nuissance when we want to use it in commercial.

A quick search in github can give some project that use deep learning for ALPR. Unfortunately, those projects are all half-done and not suitable for product.

• License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9
• ANPR
• LPRNet_Pytorch

There’re quick report about openalpr’s performance inside the paper as follow:

The ultimateALPR-SDK project is amazing. It’s worth a try.

3. Personal thinking

The performance of ultimateALPR-SDK is amazing. The price is a little tricky. If we develop a webapp then only the server license is needed and it is very cheap. But if we want to develop on edge then we need a developer license which price is hidden. Maybe in the future, when i have chance, i will contact with them to get more detail about developer license.

4. References

#. 2019: An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector #. Number Plate Datasets

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