Extracting table data from images using optical character recognition text

Akpinar, Mehmet Yasin; Emekligil, Erdem; Arslan, Secil

doi:10.1109/siu.2018.8404746

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…The remaining tasks were annotated using an annotation tool specifically designed for the form understanding. The annotation tool is based on GuiZero 7 , a high-level library built on top of tkinter 8 .…”

Section: B Annotation Proceduresmentioning

confidence: 99%

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FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

Jaume

Ekenel

Thiran

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

211

133

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In this paper, we present a new dataset for Form Understanding in Noisy Scanned Documents (FUNSD). Form Understanding (FoUn) aims at extracting and structuring the textual content of forms. The dataset comprises 200 fully annotated real scanned forms. The documents are noisy and exhibit large variabilities in their representation making FoUn a challenging task. The proposed dataset can be used for various tasks including text detection, optical character recognition (OCR), spatial layout analysis and entity labeling/linking. To the best of our knowledge this is the first publicly available dataset with comprehensive annotations addressing the FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset. The FUNSD dataset can be downloaded at https://guillaumejaume.github. io/FUNSD/.

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Section: B Annotation Proceduresmentioning

confidence: 99%

“…Applications of layout analysis range from text and non-text separation to full text segmentation of complex layouts [3]- [7]. An application closely related to FoUn is table understanding [8], [9]. In this case, the goal is to retrieve the key-value pairs that map headers from a table to the value represented by a cell.…”

Section: Introductionmentioning

confidence: 99%