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This paper presents our proposed methods to ICDAR 2021 Robust ReadingChallenge - Integrated Circuit Text Spotting and Aesthetic Assessment (ICDARRRC-ICTEXT 2021). For the text spotting task, we detect the characters onintegrated circuit and classify them based on yolov5 detection model. Webalance the lowercase and non-lowercase by using SynthText, generated data anddata sampler. We adopt semi-supervised algorithm and distillation to furtherlyimprove the model's accuracy. For the aesthetic assessment task, we add aclassification branch of 3 classes to differentiate the aesthetic classes ofeach character. Finally, we make model deployment to accelerate inference speedand reduce memory consumption based on NVIDIA Tensorrt. Our methods achieve59.1 mAP on task 3.1 with 31 FPS and 306M memory (rank 1), 78.7\% F2 score ontask 3.2 with 30 FPS and 306M memory (rank 1).

1st Place Solution to ICDAR 2021 RRC-ICTEXT End-to-end Text Spotting and Aesthetic Assessment on Integrated Circuit