End-to-End Information Extraction by Character-Level Embedding and Multi-Stage Attentional U-Net
This addresses the need for digitizing unstructured documents like invoices and receipts, offering a practical solution for real-world applications, though it is incremental as it builds on existing U-Net and attention mechanisms.
The paper tackles the problem of information extraction from document images by proposing a Multi-Stage Attentional U-Net architecture that uses character-level embeddings, which outperforms baseline U-Net by a large margin with 40% fewer parameters and improves performance in scenarios with erroneous OCR and limited training data.
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.