  Automatic License Plate Recognition (ALPR) is becoming a popular study area
and is applied in many fields such as transportation or smart city. However,
there are still several limitations when applying many current methods to
practical problems due to the variation in real-world situations such as light
changes, unclear License Plate (LP) characters, and image quality. Almost
recent ALPR algorithms process on a single frame, which reduces accuracy in
case of worse image quality. This paper presents methods to improve license
plate recognition accuracy by tracking the license plate in multiple frames.
First, the Adaptive License Plate Rotation algorithm is applied to correctly
align the detected license plate. Second, we propose a method called Character
Time-series Matching to recognize license plate characters from many
consequence frames. The proposed method archives high performance in the
UFPR-ALPR dataset which is \boldmath$96.7\%$ accuracy in real-time on RTX A5000
GPU card. We also deploy the algorithm for the Vietnamese ALPR system. The
accuracy for license plate detection and character recognition are 0.881 and
0.979<EMAIL_ADDRESS>respectively. The source code is available at
https://github.com/chequanghuy/Character-Time-series-Matching.git
