HybridTabNet: Towards Better Table Detection in Scanned Document Images

Nazir, Danish; Hashmi, Khurram Azeem; Pagani, Alain; Liwicki, Marcus; Stricker, Didier; Afzal, Muhammad Zeshan

doi:10.3390/app11188396

Cited by 21 publications

(10 citation statements)

References 65 publications

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“…The authors in [ 31 ] presented the first multistage deep neural network for table detection where the main structure of this network is based on the Cascade Mask R-CNN [ 32 ] with a composite backbone [ 33 ] having a deformable convolution for detecting tables in different scales. A novel backbone, the HybridTabNet (HTC) [ 34 ], was recently used in [ 35 ] for table detection task. The authors take advantage from this deformable backbone as a unified network for joint object detection and segmentation.…”

Section: Related Workmentioning

confidence: 99%

DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection

et al. 2023

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With the widespread use of deep learning in leading systems, it has become the mainstream in the table detection field. Some tables are difficult to detect because of the likely figure layout or the small size. As a solution to the underlined problem, we propose a novel method, called DCTable, to improve Faster R-CNN for table detection. DCTable came up to extract more discriminative features using a backbone with dilated convolutions in order to improve the quality of region proposals. Another main contribution of this paper is the anchors optimization using the Intersection over Union (IoU)-balanced loss to train the RPN and reduce the false positive rate. This is followed by a RoI Align layer, instead of the ROI pooling, to improve the accuracy during mapping table proposal candidates by eliminating the coarse misalignment and introducing the bilinear interpolation in mapping region proposal candidates. Training and testing on a public dataset showed the effectiveness of the algorithm and a considerable improvement of the F1-score on ICDAR 2017-Pod, ICDAR-2019, Marmot and RVL CDIP datasets.

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Section: Related Workmentioning

confidence: 99%

DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection

et al. 2023

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“…It also happens that the borderlines of tables may be deleted or not found at all. For this purpose, object detection models can be used as well as deep learning models developed especially for table detection [21]. Y. Huang et al modified the YOLOv3 [22] object detection model to make it suitable for table detection.…”

Section: Literature Reviewmentioning

confidence: 99%

End to End Invoice Processing Application Based on Key Fields Extraction

Arslan

2022

IEEE Access

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In this paper, an automatic invoice processing system, which is in great demand among private and public companies, was proposed. The proposed system supports all invoice file types that can be submitted by companies. Companies can easily submit invoices to the system via the web interface or email, and all invoices submitted to the system are queued and processed sequentially. If the invoice is a text file, the invoice information is extracted from the text by using template matching. If the invoice is an image, the text and table areas are detected and extracted. For table detection, we used both image processing based and YOLOv5-based deep learning method. Cell extraction was then performed from the extracted table images. As a result of these processes, all text and table cells were obtained as images and these images were converted into machine-readable text using the open-source software Tesseract OCR. Tesseract already provides trained models for English and Turkish. However, these models do not provide successful results for invoices submitted by companies in Turkish. Therefore, the new fine-tuned model trained with invoices in Turkish was used for OCR. The experimental results showed that the trained Turkish model was more accurate than the Turkish and English models provided by Tesseract. In addition, the YOLOv5-based table detection model was more accurate than the image-processing-based table detection method.

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“…The basic difference between these detectors is the cascade filter for object proposals. These detectors provide good results on a large amount of label data and used in different applications in many elds, such as document image analysis [41][42][43][44][45][46] face detection [47] and pedestrian detection [48].…”

Section: Object Detection and Its Applicationsmentioning

confidence: 99%

Mask-Aware Semi-Supervised Object Detection in Floor Plans

Shehzadi¹,

Hashmi²,

Pagani³

et al. 2022

Preprint

Self Cite

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Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labelled data. The floor-plan objects include different furniture items with multiple types of the same class, and this high inter-class similarity impacts the performance of prior methods. In this paper, we present Mask R-CNN based semi-supervised approach that provides pixel-to-pixel alignment to generate individual annotation masks for each class to mine the inter-class similarity. The semi-supervised approach has a student-teacher network that pulls information from the teacher network and feeds it to the student network. The teacher network uses unlabeled data to form pseudo-boxes, and the student network uses both unlabeled data with the pseudo boxes and labelled data as ground truth for training. It learns representations of furniture items by combining labelled and unlabeled data. On the Mask R-CNN detector with ResNet-101 backbone network, the proposed approach achieves mAP of 98.8%, 99.7%, and 99.8% with only 1%, 5% and 10% labelled data, respectively. Our experiment affirms the efficiency of the proposed approach as it outperforms the fully supervised counterpart using only 10% of the labels.

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HybridTabNet: Towards Better Table Detection in Scanned Document Images

Cited by 21 publications

References 65 publications

DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection

DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection

End to End Invoice Processing Application Based on Key Fields Extraction

Mask-Aware Semi-Supervised Object Detection in Floor Plans

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