GMN: Generative Multi-modal Network for Practical Document Information Extraction

H. Cao, Jiefeng Ma, Antai Guo, Yiqing Hu, Hao Liu, Deqiang Jiang, Yinsong Liu, Bo Ren
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引用次数: 6

Abstract

Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world. Although recent literature has already achieved competitive results, these approaches usually fail when dealing with complex documents with noisy OCR results or mutative layouts. This paper proposes Generative Multi-modal Network (GMN) for real-world scenarios to address these problems, which is a robust multi-modal generation method without predefined label categories. With the carefully designed spatial encoder and modal-aware mask module, GMN can deal with complex documents that are hard to serialized into sequential order. Moreover, GMN tolerates errors in OCR results and requires no character-level annotation, which is vital because fine-grained annotation of numerous documents is laborious and even requires annotators with specialized domain knowledge. Extensive experiments show that GMN achieves new state-of-the-art performance on several public DIE datasets and surpasses other methods by a large margin, especially in realistic scenes.
实用文档信息抽取的生成式多模态网络
文档信息提取(DIE)由于其在现实世界中的各种先进应用而受到越来越多的关注。虽然最近的文献已经取得了有竞争力的结果,但这些方法在处理具有噪声OCR结果或变化布局的复杂文档时通常会失败。针对这些问题,本文提出了基于现实场景的生成式多模态网络(GMN),它是一种无需预定义标签类别的鲁棒多模态生成方法。通过精心设计的空间编码器和模态感知掩码模块,GMN可以处理难以序列化成顺序的复杂文档。此外,GMN容忍OCR结果中的错误,并且不需要字符级注释,这一点至关重要,因为对大量文档进行细粒度注释非常费力,甚至需要具有专门领域知识的注释者。大量的实验表明,GMN在几个公共DIE数据集上取得了新的最先进的性能,并且在很大程度上超过了其他方法,特别是在真实场景中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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