检索结果-内蒙古大学图书馆

CasTabDetectoRS: Cascade Network for table detection in Document Images with Recursive Feature Pyramid and Switchable Atrous Convolution

引用

JOURNAL OF IMAGING 2021年第10期7卷 214-214页

作者： Hashmi, Khurram Azeem Pagani, Alain Liwicki, Marcus Stricker, Didier Afzal, Muhammad Zeshan Tech Univ Kaiserslautern Dept Comp Sci D-67663 Kaiserslautern Germany Tech Univ Kaiserslautern Mindgarage D-67663 Kaiserslautern Germany German Res Inst Artificial Intelligence DFKI D-67663 Kaiserslautern Germany Lulea Univ Technol Dept Comp Sci S-97187 Lulea Sweden

table detection is a preliminary step in extracting reliable information from tables in scanned document images. We present CasTabDetectoRS, a novel end-to-end trainable table detection framework that operates on Cascade Mask R-CNN, including Recursive Feature Pyramid network and Switchable Atrous Convolution in the existing backbone architecture. By utilizing a comparativelyightweight backbone of ResNet-50, this paper demonstrates that superior results are attainable without relying on pre- and post-processing methods, heavier backbone networks (ResNet-101, ResNeXt-152), and memory-intensive deformable convolutions. We evaluate the proposed approach on five different publicly available table detection datasets. Our CasTabDetectoRS outperforms the previous state-of-the-art results on four datasets (ICDAR-19, tableBank, UNLV, and Marmot) and accomplishes comparable results on ICDAR-17 POD. Upon comparing with previous state-of-the-art results, we obtain a significant relative error reduction of 56.36%, 20%, 4.5%, and 3.5% on the datasets of ICDAR-19, tableBank, UNLV, and Marmot, respectively. Furthermore, this paper sets a new benchmark by performing exhaustive cross-datasets evaluations to exhibit the generalization capabilities of the proposed method.

关键词： table detection table recognition cascade Mask R-CNN atrous convolution recursive feature pyramid networks document image analysis deep neural networks computer vision object detection

来源：评论

学校读者我要写书评

暂无评论

table detection Using Boundary Refining via Corner Locating 2nd

Table Detection Using Boundary Refining via Corner Locating

引用

2nd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Sun, Ningning Zhu, Yuanping Hu, Xiaoming Tianjin Normal Univ 393 Binshuixi Rd Tianjin Peoples R China

ISBN: (纸本)9783030316532;9783030316549

table detection based on bounding-box method has achieved remarkable results. However, there still exists inaccurate table boundary locating. In this paper, a table detection method is proposed. Firstly, coarse table detection is implemented through Faster R-CNN. Secondly, corner locating is implemented through RPN and refined through Fast R-CNN. Corner grouping and filtering are implemented through post-processing algorithms. Therefore, unreliable corners are filtered. Thirdly, table boundaries are refined via reliable corners. Experimental results show that the proposed method obviously improves the precision of table boundary locating. We test on ICDAR2017 POD dataset, our method achieves an F-measure of 95.3%. Compared to Faster R-CNN method, the proposed method significantly increases by 3.2% in F-measure. Moreover, our method increases by 3.3% at pixel-level localization.

关键词： table detection Corner locating table boundary refining

来源：评论

学校读者我要写书评

暂无评论

Cloud Computer Research on table detection Model Based on the DC-LSTM Model

Cloud Computer Research on Table Detection Model Based on th...

引用

作者： Xingming Zhang Yunchao Bai Naike Wei Huadong Pan Jun Yin ZheJiang Dahua Technology CO. LTD.

In view of the fact that tables are not easy to detect, this paper designs a table detection model based on the DC-LSTM module, which references the Convolutional Long Short-Term Memory(ConvLSTM). The model uses the backbone network of target detection to extract convolution features. The feature pyramid network is used to complete the detection task, and the DC-LSTM module is embedded in a special position in the feature pyramid network. In order to evaluate the performance of the DC-LSTM module, we added the DCLSTM module to the YOLOv3, SSD network. Specifically, we added the DC-LSTM module to the YOLOv3 network, the new model can achieve an accuracy of more than 98% on the tableBank data set and own data set. The model in this paper can realize the automatic extraction of table document images, which is of great significance for the realization of automated data collection.

关键词： Deep learning table detection The DC-LSTM module

来源：评论

学校读者我要写书评

暂无评论

Feature Engineering meets Deep Learning: A Case Study on table detection in Documents

Feature Engineering meets Deep Learning: A Case Study on Tab...

引用

APRS International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Shahzad, Muhammad Ali Noor, Rabeya Ahmad, Sheraz Mian, Ajmal Shafait, Faisal NUST SEECS Islamabad Pakistan NCAI Deep Learning Lab Islamabad Pakistan German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany Univ Western Australia Perth WA Australia

ISBN: (纸本)9781728138572

Traditional computer vision approaches heavily relied on hand-crafted features for tasks such as visual object detection and recognition. The recent success of deep learning in automatically extracting representative and powerful features from images has brought a paradigm shift in this area. As a side effect, decades of research into hand-crafted features is considered outdated. In this paper, we present an approach for table detection in which we leverage a deep learning based table detection model with hand-crafted features from a classical table detection method. We demonstrate that by using a suitable encoding of hand-crafted features, the deep learning model is able to perform better at the detection task. Experiments on publicly available UNLV dataset show that the presented method achieves an accuracy comparable with the state-of-the-art deep learning methods without the need of extensive hyper-parameter tuning.

关键词： document image analysis table detection machine learning

来源：评论

学校读者我要写书评

暂无评论

A Robust table detection Method for Distortion in Image Acquired from Camera 45

A Robust Table Detection Method for Distortion in Image Acqu...

引用

45th Annual Conference of the IEEE Industrial Electronics Society (IECON)

作者： Nakaigawa, Toshiya Mashiyama, Yoshiki Mitsukura, Yasue Hamada, Nozomu Keio Univ Grad Sch Integrated Design Engn Yokohama Kanagawa Japan Keio Univ Syst Design Engn Yokohama Kanagawa Japan Keio Univ Yokohama Kanagawa Japan

ISBN: (纸本)9781728148786

In this paper, the robust table detection method for distortion is proposed in image acquired by camera. Images acquired by the camera contain distortion due to the curvature of the paper and it makes difficult to detect the table in images. In order to address this issue, a method of dividing the frame line of the table in the vertical direction and the horizontal direction and detecting the frame lines by curve approximation in each direction is proposed. Dividing the frame lines in each direction, it is possible to simplify the multiple curve detection. As a result, the lines detection accuracy in the curved surface image was 99.4%. In addition, similar results were obtained for curved images rotated by 90 degrees. This method can cope with distortion in both the vertical and horizontal directions. From these results, it was confirmed the effectiveness of the proposed method.

关键词： image processing camera-based document analysis and recognition (CBDAR) table detection

来源：评论

学校读者我要写书评

暂无评论

ICDAR 2019 competition on table detection and recognition (cTDaR) 15

ICDAR 2019 competition on table detection and recognition (c...

引用

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Gao, Liangcai Huang, Yilun Dejean, Herve Meunier, Jean-Luc Yan, Qinqin Fang, Yu Kleber, Florian Lang, Eva ICST Peking University China Naver Labs Europe Meylan France State Key Laboratory of Digital Publishing Technology Founder Group Co. LTD. China Computer Vision Lab TU Wien Vienna1040 Austria Archiv des Bistums Passau Passau Germany

ISBN: (纸本)9781728128610

The cTDaR competition aims at benchmarking state-of-the-art table detection (TRACK A) and table recognition (TRACK B) methods. In particular, we wish to investigate and compare general methods that can reliably and robustly identify the table regions within a document image on the one hand, and the table structure on the other hand. Due to the presence of hand-drawn tables and handwritten text, the methods must be robust against various noise conditions, interfering annotations, and variations of the tables. Two new challenging datasets were created to test the behaviour of state-of-the-art table detection and recognition systems on real world data. One dataset consists of modern documents, while the other consists of archival documents with presence of hand-drawn tables and handwritten text. The evaluation scheme is adapted from the ICDAR 2013 table competition. We received results of Track A from 11 teams and results of Track B from 2 teams. Results for Track A are very good for the top participants. The winner and his runner-up are very close while using very different approaches. Track B was more challenging and only one participant was able to produce good results. © 2019 IEEE.

关键词： Archival documents Modern tables table detection table recognition

来源：评论

学校读者我要写书评

暂无评论

table detection in Document Images using Foreground and Background Features

Table Detection in Document Images using Foreground and Back...

引用

International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Arif, Saman Shafait, Faisal NUST SEECS Islamabad Pakistan NCAI Deep Learning Lab Islamabad Pakistan

ISBN: (纸本)9781538666029

table detection is an important step in many document analysis systems. It is a difficult problem due to the variety of table layouts, encoding techniques and the similarity of tabular regions with non-tabular document elements. Earlier approaches of table detection are based on heuristic rules or require additional PDF metadata. Recently proposed methods based on machine learning have shown good results. This paper demonstrates performance improvement to these table detection techniques. The proposed solution is based on the observation that tables tend to contain more numeric data and hence it applies color coding/ coloration as a signal for telling apart numeric and textual data. Deep learning based Faster R-CNN is used for detection of tabular regions from document images. To gauge the performance of our proposed solution, publicly available UNLV dataset is used. Performance measures indicate improvement when compared with best in-class strategies.

关键词： table detection heuristic rules document image analysis metadata

来源：评论

学校读者我要写书评

暂无评论

DeCNT: Deep Deformable CNN for table detection

引用

IEEE ACCESS 2018年 6卷 74151-74161页

作者： Siddiqui, Shoaib Ahmed Malik, Muhammad Imran Agne, Stefan Dengel, Andreas Ahmed, Sheraz German Res Ctr Artificial Intelligence D-67663 Kaiserslautern Germany Univ Kaiserslautern Dept Comp Sci D-67663 Kaiserslautern Germany Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad Pakistan

This paper presents a novel approach for the detection of tables present in documents, leveraging the potential of deep neural networks. Conventional approaches for table detection rely on heuristics that are error prone and specific to a dataset. In contrast, the presented approach harvests the potential of data to recognize tables of arbitrary layout. Most of the prior approaches for table detection are only applicable to PDFs, whereas, the presented approach directly works on images making it generally applicable to any format. The presented approach is based on a novel combination of deformable CNN with faster R-CNN/FPN. Conventional CNN has a fixed receptive field which is problematic for table detection since tables can be present at arbitrary scales along with arbitrary transformations (orientation). Deformable convolution conditions its receptive field on the input itself allowing it to mold its receptive field according to its input. This adaptation of the receptive field enables the network to cater for tables of arbitrary layout. We evaluated the proposed approach on two major publicly available table detection datasets: ICDAR-2013 and ICDAR-2017 POD. The presented approach was able to surpass the state-of-the-art performance on both ICDAR-2013 and ICDAR-2017 POD datasets with a F-measure of 0.994 and 0.968, respectively, indicating its effectiveness and superiority for the task of table detection.

关键词： Deep learning representation learning convolutional neural networks object detection deformable convolution table detection table spotting faster R-CNN FPN

来源：评论

学校读者我要写书评

暂无评论

Automatic table detection and Retention from Scanned Document Images via Analysis of Structural Information 4

Automatic Table Detection and Retention from Scanned Documen...

引用

4th International Conference on Image Information Processing (ICIIP)

作者： Ranka, Varsha Patil, Shubham Patni, Shubham Raut, Tushar Mehrotra, Kapil Gupta, Manish Kumar PICT Dept Comp Engn Pune Maharashtra India Ctr Dev Adv Comp Pune Maharashtra India

ISBN: (纸本)9781509067343

The problem of automatic table detection has always been a great topic of debate in the field of Document Analysis and Recognition (DAR). Digital documents are efficient than their printed counterparts for storage, maintenance and republishing. Being a non-textual object of a document, tables prevent OCR system to digitize a document perfectly and distorts layout and structure of digitized documents. There is no available algorithm or method which solves this problem for all possible types of tables. This paper tackles the problem of table detection and retention by proposing a bi-modular approach based on structural information of tables. This structural information includes bounding lines, row/column separators and space between columns. Through analysis of these properties, our experiments on a dataset of above 600 images consisting of more than 829 tables have detected 90% of the table correctly.

关键词： Optical Character Recognition table detection table Retention Layout analysis Document Analysis and Recognition

来源：评论

学校读者我要写书评

暂无评论

table detection from Slide Images 7th

Table Detection from Slide Images

引用

7th Pacific-Rim Symposium on Image and Video Technology (PSIVT)

作者： Che, Xiaoyin Yang, Haojin Meinel, Christoph Univ Potsdam Hasso Plattner Inst Prof Dr Helmert Str 2-3 D-14482 Potsdam Germany

ISBN: (纸本)9783319294513;9783319294506

In this paper we propose a solution to detect tables from slide images. Presentation slides are one type of document with growing importance. But the layout difference between slides and traditional documents makes many existing table detection methods less effective on slides. The proposed solution works with both high-resolution slide images from digital files and low-resolution slide screenshots from videos. By taking OCR (Optical Character Recognition) as initial step, a heuristic analysis on page layout focuses not only on the table structure but also the textual content. The evaluation result shows that the proposed solution achieves an approximate accuracy of 80 %. It is way better than the open-source academic solution Tesseract and also outperforms the commercial software ABBYY FineReader, which is supposed to be one of the best table detection tools.

关键词： table detection Slide image table structure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：