Reading Scene Text with Aggregated Temporal Convolutional Encoder

Abstract

Reading scene text in the natural image is of fundamental importance in many real-world problems. Text recognition has a profound effect on information processing by enabling automated extraction and interpretation. Recent scene text recognition methods employ the encoder-decoder framework, which constructs the encoder by obtaining the visual representations based on the last layer of the backbone network and then feeding them into a sequence model. In this article, we propose a novel encoder structure that performs the feature extractor and the sequence modeling within a unified framework. The introduced Aggregated Temporal Convolutional Encoder (ATCE) first incorporates the temporal convolutional layers to consider the long-term temporal relationship in the encoder stage. The aggregation of these temporal convolution modules is designed to utilize visual features from different levels, by augmenting the standard architecture with deeper aggregation to better fuse information across modules. We also study the impact of different attention modules in convolutional blocks for learning accurate text representations. We conduct comparisons on several scene text recognition benchmarks for both Chinese and English; the experiments demonstrate the complementary ability with different decoder variants and the effectiveness of our proposed approach.