Future work

Although the methods in this work already include complete functions to recognize license plates from gray-level images, there still exist some issues worthy of future studies.

First, although the SSB method can be designed to do full scale search, the computation for a full scale search is still too heavy by pure software for real-time applications. A hardware accelerator can be studied to speed up the extraction process. Besides, the SSB method may not be able to extract character candidates accurately if the resolution of the image is low. Some methods to improve the successful extraction rate in low resolution must be researched.

Second, although the voting boundary method is helpful to correct characters from deformation, it is sometimes inaccurate to correct characters of non-linear edges like “S” or “D”

or “Q” from certain deformations due to their curvature edges. Besides, it cannot work properly if the license plate includes dirty smudges around characters or the resolution is low.

Third, the AGPV method requires manual decisions such as selecting standard characters by human eyes. It is better to be improved by some systematic procedures to do automatically training from various input images. In addition, currently the AGPV method may degrade recognition rate seriously if the geometric deformation of characters is not fully corrected in the normalization stage. How to improve the recognition rate when the test characters undergo certain degree of geometric deformation is also an important topic for future studies. Moreover, the computational complexity of the AGPV method is heavy and still needs improvement in the future.

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