Abstract: Aiming at the erroneous literacy caused by irregular interference and text adhesion in text images, this paper proposes a text image restoration model based on partial convolution operations. This paper studies and analyzes the text image characteristics of several common fonts, establishes a text image database, and integrates it with the interference mask database, then evaluates the repair effect of the model, and conducts classification tests on different levels of repair. Experiments have proved that the text model predicts the missing parts based on the existing parts of the current text under the premise of ensuring that the original text information is not lost. The peak signal-to-noise ratio is up to 32.46 dB, the structural similarity is up to 0.954, and the best loss value is up to 0.015. The text recognition rate after repair is 27.85% higher than that before repair. After repairing the defective pictures of four ancient scripts, including official script, seal script, oracle bone inscriptions, and running script, the peak signal-to-noise ratio reached 30.46 dB, and the structural similarity reached 0.964. 

Key words: text image repair, partial convolution, optical character recognition, deep learning