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%.