A New Strategy for Improving the Accuracy in Scene Text Recognition

Scene text recognition is the process of recognizing and converting text in natural images, such as street signs or billboards, into machine-readable text. However, there are some complexities, background variability, and even serious interference, and the text may also have diversity, which can lead to errors in recognition. This paper proposes an optimized text recognition strategy that can reduce errors by combining convolutional neural networks and recurrent neural networks to achieve accurate recognition of text in text images. A sequence learning model based on recurrent neural networks was used to process text sequences of different lengths, and CTC Loss was used to train the model. In addition, we also used data augmentation methods to increase the diversity of training data, in order to improve the robustness and generalization ability of the model. We conducted experiments on multiple public datasets to validate the performance and effectiveness of our proposed model. The experimental results showed that our method has reached the level of state-of the-art methods in terms of accuracy in detection results.