TNCR: Table net detection and classification dataset

Abdallah A. Berendeyev A. Nuradin I. Nurseitov D.
7 February 2022 Elsevier B.V.

Neurocomputing
2022 #473 79 - 97 pp.

We present TNCR, a new table dataset with varying image quality collected from open access websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 labeled tables with approximately 6621 images. In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Deformable DERT with Resnet-50 Backbone Network achieves the highest performance compared to other methods with a precision of 86.7%, recall of 89.6%, and f1 score of 88.1% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification and structure recognition. The dataset and trained model checkpoints are available at https://github.com/abdoelsayed2016/TNCR_Dataset.

Convolutional neural networks , Deep learning , Document processing , Image processing , Page object detection , Table detection

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KazMunayGas Engineering LLP, Nur-Sultan, Nur-Sultan, 010000, Kazakhstan
Artificial Intelligence Laboratory, Satbayev University, Almaty, Almaty, 050013, Kazakhstan
Department of Machine Learning & Data Science, Satbayev University, Almaty, Almaty, 050013, Kazakhstan
Information Technology Department, Assiut University, Assiut, Assiut, 71515, Egypt

KazMunayGas Engineering LLP
Artificial Intelligence Laboratory
Department of Machine Learning & Data Science
Information Technology Department

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