Simulation Research on Large Language Model of Complex OCR Scene Based on Reinforcement Learning Algorithm Optimization
Yifeng Xue · 2023
Facing the challenges of text diversity, noise interference and complex structure, the traditional optical character recognition (OCR) system shows some limitations in practical application. In order to solve this problem, this study introduces the idea of Reinforcement Learning (RL) to improve the accuracy and robustness of the model in complex OCR scenes. Firstly, this paper designs an optimization framework of OCR system based on RL, and verifies its effectiveness in error correction, error identification and text structure optimization through a large number of simulation experiments. Experimental results show that RL algorithm can significantly improve the performance of large language model in complex scenes, especially in dealing with diverse texts and noise interference. At the same time, the proposed model is compared with the traditional model, and the obvious advantages of the proposed model in recognition performance are observed. By comparing the convergence speed and generalization performance of model training, the unique advantages of RL in large language model optimization are verified. This study not only provides in-depth empirical support for the application of RL in OCR system, but also provides beneficial enlightenment for the challenge of large language model in dealing with complex OCR scenes. It is expected that these findings can provide valuable reference and guidance for the research and practice in related fields.