DPF-S2S: A novel dual-pathway-fusion-based sequence-to-sequence text recognition model

In this paper, a novel dual-pathway-fusion-based sequence-to-sequence learning model (DPF-S2S) is pro-posed for text recognition in the wild, which mainly focuses on enriching the spatial information and extracting high-dimensional representation features to assist decoding. In particular, a double alignment module is developed to solve the problem of text misalignment, where both position and vision informa-tion are well considered. Moreover, a global fusion module is deployed to enrich 2D information in the aligned attention maps, which benefits accurate recognition from complicated scenes with arbitrary text shapes and poor imaging conditions. Benchmark evaluations on seven datasets have demonstrated the superiority of proposed DPF-S2S model in comparison to other state-of-the-art text recognition methods, which presents great competitiveness on identifying texts in both regular and irregular scenes. In addi-tion, extensive ablation studies have been carried out, which validate the effectiveness of applied strate-gies in proposed DPF-S2S.(c) 2022 Elsevier B.V. All rights reserved.