2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)最新文献_第9页

A Man-Machine Cooperating System Based on the Generalized Reject Model 基于广义拒绝模型的人机协作系统

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.218

Shunichi Kimura, E. Tanaka, Masanori Sekino, Takuya Sakurai, Satoshi Kubota, Ikken So, Y. Koshi

引用次数: 1

DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans DANIEL:用于自动分析和检索建筑平面图的深度架构

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.76

Divya Sharma, Nitin Gupta, C. Chattopadhyay, S. Mehta

{"title":"DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans","authors":"Divya Sharma, Nitin Gupta, C. Chattopadhyay, S. Mehta","doi":"10.1109/ICDAR.2017.76","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.76","url":null,"abstract":"Automatically finding out existing building layouts from a repository is always helpful for an architect to ensure reuse of design and timely completion of projects. In this paper, we propose Deep Architecture for fiNdIng alikE Layouts (DANIEL). Using DANIEL, an architect can search from the existing projects repository of layouts (floor plan), and give accurate recommendation to the buyers. DANIEL is also capable of recommending the property buyers, having a floor plan image, the corresponding rank ordered list of alike layouts. DANIEL is based on the deep learning paradigm to extract both low and high level semantic features from a layout image. The key contributions in the proposed approach are: (i) novel deep learning framework to retrieve similar floor plan layouts from repository; (ii) analysing the effect of individual deep convolutional neural network layers for floor plan retrieval task; and (iii) creation of a new complex dataset ROBIN (Repository Of BuildIng plaNs), having three broad dataset categories with 510 real world floor plans.We have evaluated DANIEL by performing extensive experiments on ROBIN and compared our results with eight different state-of-the-art methods to demonstrate DANIEL’s effectiveness on challenging scenarios.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122878364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 49

Early Recognition of Handwritten Gestures Based on Multi-Classifier Reject Option 基于多分类器拒绝选项的手写手势早期识别

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.43

Zhaoxin Chen, É. Anquetil, C. Viard-Gaudin, H. Mouchère

引用次数: 5

Convolutional Neural Networks for Figure Extraction in Historical Technical Documents 历史技术文档中图形提取的卷积神经网络

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.134

Chun-Nam Yu, Caleb C. Levy, I. Saniee

引用次数: 2

Localizing and Recognizing Labels for Multi-Panel Figures in Biomedical Journals 生物医学期刊中多版面图形标签的定位与识别

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.128

Jie Zou, Sameer Kiran Antani, G. Thoma

{"title":"Localizing and Recognizing Labels for Multi-Panel Figures in Biomedical Journals","authors":"Jie Zou, Sameer Kiran Antani, G. Thoma","doi":"10.1109/ICDAR.2017.128","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.128","url":null,"abstract":"Multi-panel figures are common in biomedical journals. Often the subpanels are of different types, e.g. x-ray, microscopy, sketch, etc. Visual information retrieval of such figures can significantly benefit from Panel Label Recognition techniques that index figures for search engines, image content tagging, and correlating with figure (sub)captions. It is a challenging task due to large variation in the label locations, sizes, contrast to background, etc. In this work, we propose a 3-stage recognition algorithm. The first stage is formulated as object detection, where we extract Histograms of Oriented Gradient (HOG) features and train a linear Support Vector Machine (SVM) classifier. Label candidates are detected using sliding windows at different locations and scales. We also trained a convolutional deep neural network (CNN) to remove false positives. The second stage is formulated as image classification. We trained a 50-class RBF SVM classifier and estimate the posterior probabilities of each candidate label. The last stage is formulated as sequence classification. We used a beam search algorithm on the posterior probabilities estimated in the second stage along with a set of label sequence constraints to select an optimal label sequence. The algorithm is trained on 9,642 figures, and evaluated on the remaining 1,000 figures shows that the proposed algorithm achieves good precision and recall.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129823379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Binarizing Document Images Acquired with Portable Cameras 便携式相机获取的文件图像二值化

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.348

R. Lins, R. Bernardino, D. Jesus, José Mário Oliveira

引用次数: 10

A Rectangle Mining Method for Understanding the Semantics of Financial Tables 一种理解财务表语义的矩形挖掘方法

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.52

Xilun Chen, Laura Chiticariu, Marina Danilevsky, A. Evfimievski, P. Sen

引用次数: 10

Music Document Layout Analysis through Machine Learning and Human Feedback 通过机器学习和人类反馈分析音乐文档布局

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.259

Jorge Calvo-Zaragoza, Kecheng Zhang, Z. Saleh, Gabriel Vigliensoni, Ichiro Fujinaga

引用次数: 4

GMU: A Novel RNN Neuron and Its Application to Handwriting Recognition 一种新的RNN神经元及其在手写识别中的应用

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.176

Li Sun, Tonghua Su, Shengjie Zhou, Lijun Yu

引用次数: 6

A Comparative Study on Optical Modeling Units for Off-Line Arabic Text Recognition 阿拉伯语离线文本识别光学建模单元的比较研究

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-01 DOI: 10.1109/ICDAR.2017.170

Mohamed Benzeghiba

引用次数: 3