Deep Visual Template-Free Form Parsing

Davis, Brian L.; Morse, Bryan S.; Cohen, Scott; Price, Brian; Tensmeyer, Chris

doi:10.1109/icdar.2019.00030

Cited by 33 publications

(44 citation statements)

References 21 publications

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“…Another challenge for the information extraction techniques is to process and enhance the quality of the scanned documents, as the documents submitted by the client or supplier are generally scanned with a low-quality scanner or mobile devices. Multi-page unstructured documents consisting of tables with data spanning across different pages complicate retrieval of the correct target data from the document [54].…”

Section: ) Data Related Challengesmentioning

confidence: 99%

“…2) Domain specific datasets: Existing publicly available datasets are very task-specific; that is, they are related to the data extraction of the scientific articles or clinical information that is not generalized [93]. In handwritten datasets, various kinds of handwritings are present, even cursive text, making it challenging for the OCR to detect and extract the actual text, leading to less accurate results [54]. In such cases, the advanced OCR techniques are needed.…”

Section: ) Challenges/issues With Existing Datasetsmentioning

confidence: 99%

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Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3.The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.

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Section: ) Data Related Challengesmentioning

confidence: 99%

Section: ) Challenges/issues With Existing Datasetsmentioning

confidence: 99%

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

et al. 2021

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“…The study [23] presented a template-free form field extraction method on NAF historical handwritten filled form dataset, with a varied layout and noisy form images using Fully Convolutional Network (FCN). FCN is used along with a Heuristic Detector function for detecting the relationship between label-value pairs.…”

Section: Named Entity Recognition (Ner)mentioning

confidence: 99%

Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

2021

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The daily transaction of an organization generates a vast amount of unstructured data such as invoices and purchase orders. Managing and analyzing unstructured data is a costly affair for the organization. Unstructured data has a wealth of hidden valuable information. Extracting such insights automatically from unstructured documents can significantly increase the productivity of an organization. Thus, there is a huge demand to develop a tool that can automate the extraction of key fields from unstructured documents. Researchers have used different approaches for extracting key fields, but the lack of annotated and highquality datasets is the biggest challenge. Existing work in this area has used standard and custom datasets for extracting key fields from unstructured documents. Still, the existing datasets face some serious challenges, such as poor-quality images, domain-related datasets, and a lack of data validation approaches to evaluate data quality. This work highlights the detailed process flow for endto-end key fields extraction from unstructured documents. This work presents a high-quality, multi-layout unstructured invoice documents dataset assessed with a statistical data validation technique. The proposed multi-layout unstructured invoice documents dataset is highly diverse in invoice layouts to generalize key field extraction tasks for unstructured documents. The proposed multilayout unstructured invoice documents dataset is evaluated with various feature extraction techniques such as Glove, Word2Vec, FastText, and AI approaches such as BiLSTM and BiLSTM-CRF. We also present the comparative analysis of feature extraction techniques and AI approaches on the proposed multi-layout unstructured invoice document dataset. We attained the best results with BiLSTM-CRF model. INDEX TERMS Artificial Intelligence (AI), information extraction, key field extraction, Named Entity Recognition (NER), template-free invoice processing, unstructured data.

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“…Katti et al (2018); explore to directly work on 2D document space using grid-like convolutional models to better preserve spatial context during learning, but the performance is restrictive to the resolution of the grids. Recently, Qian et al (2019); Davis et al (2019); Liu et al (2019) propose to represent documents using graphs, where nodes define word tokens and edges describe the spatial patterns of words. show state-of-the-art performance of Graph Convolutional Networks (GCNs) (Duvenaud et al, 2015) on document understanding.…”

Section: Introductionmentioning

confidence: 99%

ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction

Lee¹,

Li²,

Wang³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph. We propose Reading Order Equivariant Positional Encoding (ROPE), a new positional encoding technique designed to apprehend the sequential presentation of words in documents. ROPE generates unique reading order codes for neighboring words relative to the target word given a word-level graph connectivity. We study two fundamental document entity extraction tasks including word labeling and word grouping on the public FUNSD dataset and a large-scale payment dataset. We show that ROPE consistently improves existing GCNs with a margin up to 8.4% F1-score. * Work done while an intern at Google Research.

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Deep Visual Template-Free Form Parsing

Cited by 33 publications

References 21 publications

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction

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