Two-Stage Pre-processing for License Recognition

Jie Zhang, Cheng Tsung Chan, Min Te Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Various financial insurance and investment application websites require customers to upload identity documents, such as vehicle licenses, to verify their identities. Manual verification of these documents is costly. Hence, there is a clear demand for automatic document recognition. This study proposes a two-stage method to pre-process a vehicle license for a better text recognition. In the first stage, the distortion that often appears in photographed documents is repaired. In the second stage, each data field is carefully located. The subsequent captured fields are then processed by a commercial text recognition software. Due to the sensitivity of vehicle licenses, it is difficult to collect enough data for model training. Consequently, artificial vehicle licenses are synthesized for model training to mitigate overfitting. In addition, an encoder is applied to reduce the background noise, remove the border crossing over text, and make the blurred text clearer before text recognition. The proposed method on a real dataset shows that the accuracy is close to 90%.

Original languageEnglish
Title of host publication51st International Conference on Parallel Processing, ICPP 2022 - Workshop Proceedings
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450394451
StatePublished - 29 Aug 2022
Event51st International Conference on Parallel Processing, ICPP 2022 - Virtual, Online, France
Duration: 29 Aug 20221 Sep 2022

Publication series

NameACM International Conference Proceeding Series


Conference51st International Conference on Parallel Processing, ICPP 2022
CityVirtual, Online


  • deep learning
  • optical character recognition
  • text detection
  • text recognition


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