Vision Grid Transformer For Document Layout Analysis
Cheng da, Chuwei Luo, Qi Zheng, Cong Yao . 2023 IEEE/CVF International Conference on Computer Vision (ICCV) 2023 – 40 citations
[Code]
[Paper]
Document pre-trained models and grid-based models have proven to be very effective on various tasks in Document AI. However, for the document layout analysis (DLA) task, existing document pre-trained models, even those pre-trained in a multi-modal fashion, usually rely on either textual features or visual features. Grid-based models for DLA are multi-modality but largely neglect the effect of pre-training. To fully leverage multi-modal information and exploit pre-training techniques to learn better representation for DLA, in this paper, we present VGT, a two-stream Vision Grid Transformer, in which Grid Transformer (GiT) is proposed and pre-trained for 2D token-level and segment-level semantic understanding. Furthermore, a new dataset named D(^4)LA, which is so far the most diverse and detailed manually-annotated benchmark for document layout analysis, is curated and released. Experiment results have illustrated that the proposed VGT model achieves new state-of-the-art results on DLA tasks, e.g. PubLayNet ((95.7%)(\rightarrow)(96.2%)), DocBank ((79.6%)(\rightarrow)(84.1%)), and D(^4)LA ((67.7%)(\rightarrow)(68.8%)). The code and models as well as the D(^4)LA dataset will be made publicly available ~https://github.com/AlibabaResearch/AdvancedLiterateMachinery.