Textual Grounding for Open-vocabulary Visual Information Extraction in Layout-diversified Documents

Mengjun Cheng, Chengquan Zhang, Chang Liu*, Yuke Li, Bohan Li, Kun Yao, Xiawu Zheng, Rongrong Ji, Jie Chen ;

"Current methodologies have achieved notable success in the closed-set visual information extraction (VIE) task, while the exploration into open-vocabulary settings is comparatively underdeveloped, which is practical for individual users in terms of inferring information across documents of diverse types. Existing proposal solutions, including named entity recognition methods and large language model-based methods, fall short in processing the unlimited range of open-vocabulary keys and missing explicit layout modeling. This paper introduces a novel method for tackling the given challenge by transforming the process of categorizing text tokens into a task of locating regions based on given queries also called textual grounding. Particularly, we take this a step further by pairing open-vocabulary key language embedding with corresponding grounded text visual embedding. We design a document-tailored grounding framework by incorporating layout-aware context learning and document-tailored two-stage pre-training, which significantly improves the model’s understanding of documents. Our method outperforms current proposal solutions on the SVRD benchmark for the open-vocabulary VIE task, offering lower costs and faster inference speed. Specifically, our method infers 20× faster than the QwenVL model and achieves an improvement of 24.3% in the F-score metric."