RMFPN: End-to-End Scene Text Recognition Using Multi-Feature Pyramid Network

Abstract

Scene text recognition (STR) plays an important role in various computer vision activities. STR has been a desirable research topic in the computer community, and deep learning-based STR methods have gained tremendous outcomes over the past few years. Earlier state-of-the-art scene text recognition approaches even deliver a notable quantity of inaccurate yields when applied to images caught in real-world environments. Because these images lose precise text content information, previous methods generate less robust features and semantic information about text content. To address this issue, we propose a new approach called Residual Multi-Feature Pyramid Network(RMFPN), which integrates ResNet and Multi-Feature Pyramid Networks to grab multi-level relations, enrich the functionality, and generalization of the feature extractor. We build RMFPN with two convolutional pyramids as a feature extractor, which improves the robustness of features and semantic information to endure scene text recognition of various scales. Comprehensive experiments on diverse datasets demonstrate that our proposed method can acquire significant performance accuracy. The proposed RMFPN acquires a 0.61%, 1.2%, 1%, and 0.2% improvement on SVT, IC15, SVTP, and CUTE datasets.

Keywords

• Scene text recognition
• deep learning
• convolutional neural network
• transformer
• multi-feature pyramid network