End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net

Tuan-Anh Nguyen Dang; Dat-Thanh Nguyen

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arXiv · 2106.00952

End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net

Tuan-Anh Nguyen Dang·Dat-Thanh Nguyen

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Abstract

Information extraction from document images has received a lot of attention recently, due to the need for digitizing a large volume of unstructured documents such as invoices, receipts, bank transfers, etc. In this paper, we propose a novel deep learning architecture for end-to-end information extraction on the 2D character-grid embedding of the document, namely the \textit{Multi-Stage Attentional U-Net}. To effectively capture the textual and spatial relations between 2D elements, our model leverages a specialized multi-stage encoder-decoders design, in conjunction with efficient uses of the self-attention mechanism and the box convolution. Experimental results on different datasets show that our model outperforms the baseline U-Net architecture by a large margin while using 40\% fewer parameters. Moreover, it also significantly improved the baseline in erroneous OCR and limited training data scenario, thus becomes practical for real-world applications.

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BibTeX RIS

Tuan-Anh Nguyen Dang, Dat-Thanh Nguyen. 2021-06-02. End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net. https://arxiv.org/abs/2106.00952

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