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Mobile Networks and Applications
Published in: Mobile Networks and Applications 4/2021

30-04-2021

ClusTi: Clustering Method for Table Structure Recognition in Scanned Images

Authors: Arthur Zucker, Younes Belkada, Hanh Vu, Van Nam Nguyen

Published in: Mobile Networks and Applications | Issue 4/2021

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Abstract

OCR (Optical Character Recognition) for scanned paper invoices is very challenging due to the variability of 19 invoice layouts, different information fields, large data tables, and low scanning quality. In this case, table structure recognition is a critical task in which all rows, columns, and cells must be accurately positioned and extracted. Existing methods such as DeepDeSRT only dealt with high-quality born-digital images (e.g., PDF) with low noise and apparent table structure. This paper proposes an efficient method called CluSTi (Clustering method for recognition of the Structure of Tables in invoice scanned Images). The contributions of CluSTi are three-fold. Firstly, it removes heavy noises in the table images using a clustering algorithm. Secondly, it extracts all text boxes using state-of-the-art text recognition. Thirdly, based on the horizontal and vertical clustering algorithm with optimized parameters, CluSTi groups the text boxes into their correct rows and columns, respectively. The method was evaluated on three datasets: i) 397 public scanned images; ii) 193 PDF document images from ICDAR 2013 competition dataset; and iii) 281 PDF document images from ICDAR 2019’s numeric tables. The evaluation results showed that CluSTi achieved an F1-score of 87.5%, 98.5%, and 94.5%, respectively. Our method also outperformed DeepDeSRT with an F1-score of 91.44% on only 34 images from the ICDAR 2013 competition dataset. To the best of our knowledge, CluSTi is the first method to tackle the table structure recognition problem on scanned images.
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Metadata
Title
ClusTi: Clustering Method for Table Structure Recognition in Scanned Images
Authors
Arthur Zucker
Younes Belkada
Hanh Vu
Van Nam Nguyen
Publication date
30-04-2021
Publisher
Springer US
Published in
Mobile Networks and Applications / Issue 4/2021
Print ISSN: 1383-469X
Electronic ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-021-01759-9

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