  This paper presents our proposed methods to ICDAR 2021 Robust Reading
Challenge - Integrated Circuit Text Spotting and Aesthetic Assessment (ICDAR
RRC-ICTEXT 2021). For the text spotting task, we detect the characters on
integrated circuit and classify them based on yolov5 detection model. We
balance the lowercase and non-lowercase by using SynthText, generated data and
data sampler. We adopt semi-supervised algorithm and distillation to furtherly
improve the model's accuracy. For the aesthetic assessment task, we add a
classification branch of 3 classes to differentiate the aesthetic classes of
each character. Finally, we make model deployment to accelerate inference speed
and reduce memory consumption based on NVIDIA Tensorrt. Our methods achieve
59.1 mAP on task 3.1 with 31 FPS and 306M memory (rank 1), 78.7\% F2 score on
task 3.2 with 30 FPS and 306M memory (rank 1).
