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

Open Evaluation Tool for Layout Analysis of Document Images 文档图像布局分析开放评估工具

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-23 DOI: 10.1109/ICDAR.2017.311

Michele Alberti, Manuel Bouillon, R. Ingold, M. Liwicki

引用次数: 24

ICDAR2017 Competition on Recognition of Early Indian Printed Documents - REID2017 早期印度印刷文件识别竞赛- REID2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-15 DOI: 10.1109/ICDAR.2017.230

C. Clausner, A. Antonacopoulos, T. Derrick, S. Pletschacher

引用次数: 10

ICDAR2017 Competition on Recognition of Documents with Complex Layouts - RDCL2017 ICDAR2017复杂布局文档识别竞赛- RDCL2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-15 DOI: 10.1109/ICDAR.2017.229

C. Clausner, A. Antonacopoulos, S. Pletschacher

引用次数: 56

Enhancing Table of Contents Extraction by System Aggregation 通过系统聚合增强目录提取

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-13 DOI: 10.1109/ICDAR.2017.48

Thi-Tuyet-Hai Nguyen, A. Doucet, Mickaël Coustaty

引用次数: 16

ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script ICDAR2017中世纪拉丁手写体分类竞赛

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-12 DOI: 10.1109/ICDAR.2017.224

F. Cloppet, V. Eglin, Marlene Helias-Baron, V. C. Kieu, N. Vincent, D. Stutzmann

{"title":"ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script","authors":"F. Cloppet, V. Eglin, Marlene Helias-Baron, V. C. Kieu, N. Vincent, D. Stutzmann","doi":"10.1109/ICDAR.2017.224","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.224","url":null,"abstract":"This paper presents the results of the ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers). This work follows a competition at ICFHR2016 and aims at providing a rich annotated database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. We proposed four independent classification tasks which attracted 10 registered teams, with 6 submitted classifiers from 4 participants. Those classifiers are trained on a set of 3540 images with their ground truths. In task 1 (Script classification) and task 3 (Date classification), the classifiers have been evaluated by a test set of 2000 greyscale, tiff, 300 dpi images. In task 2 (Script classification) and task 4 (Date classification), the test set consists of 1000 images in different formats, resolutions and color representation. The best scores are respectively 85.2% for task 1, 76.5% for task 2, 59% for task 3, and 49.9% for task 4. An analysis based on the matrix of confusion of each classifier is also given.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129435840","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: 27

A Study of the Factors Influencing OCR Stability for Hybrid Security 影响混合安全OCR稳定性的因素研究

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-12 DOI: 10.1109/ICDAR.2017.388

Sébastien Eskenazi, Petra Gomez-Krämer, J. Ogier

引用次数: 3

Page Retrieval System in Digitized Historical Books Based on Error-Tolerant Subgraph Matching 基于容错子图匹配的数字化史书页面检索系统

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-09 DOI: 10.1109/ICDAR.2017.193

Maroua Mehri, P. Héroux, Julien Lerouge, R. Mullot

{"title":"Page Retrieval System in Digitized Historical Books Based on Error-Tolerant Subgraph Matching","authors":"Maroua Mehri, P. Héroux, Julien Lerouge, R. Mullot","doi":"10.1109/ICDAR.2017.193","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.193","url":null,"abstract":"Developing smart ways of interacting with scanners is one of the emerging needs identified by numerous digitization professionals. To achieve better interaction with scanners, the research community in historical document image analysis is particularly interested in providing reliable tools for computer-aided indexing and retrieval of historical document images. Thus, we propose in this article a method able to retrieve from a digitized historical book, pages having layout and/or content which meet the user-defined query. Amongst the user-defined queries we focus on the transition pages (e.g. title pages of chapter, end-of-chapter and end-of-act) and pages containing a particular content component or a group of patterns (e.g. ornaments, illustrations and drop caps) in our work. The method adopted in this work is firstly based on using low-level features (texture, shape and geometric descriptors) to represent each page in the form of a graph-based signature. Then, a set of costs is estimated using an error-tolerant subgraph isomorphism algorithm in order to measure the similarity between the user-defined query formulated in terms of a pattern graph and the different subgraphs of the book page signatures and to find book pages similar to the user-defined query. To illustrate the effectiveness of the proposed method, a thorough experimental study has been conducted with quantitative observations obtained from a large number of queries having different contents and structures.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121220287","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: 1

Cortical-Inspired Open-Bigram Representation for Handwritten Word Recognition 手写单词识别的皮质启发的开放双图表示

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-09 DOI: 10.1109/ICDAR.2017.21

Théodore Bluche, Christopher Kermorvant, C. Touzet, H. Glotin

{"title":"Cortical-Inspired Open-Bigram Representation for Handwritten Word Recognition","authors":"Théodore Bluche, Christopher Kermorvant, C. Touzet, H. Glotin","doi":"10.1109/ICDAR.2017.21","DOIUrl":"https://doi.org/10.1109/ICDAR.2017.21","url":null,"abstract":"Recent research in the cognitive process of reading hypothesized that we do not read words by sequentially recognizing letters, but rather by identifing open-bigrams, i.e. couple of letters that are not necessarily next to each other. In this paper, we evaluate an handwritten word recognition method based on original open-bigrams representation. We trained Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) to predict open-bigrams rather than characters, and we show that such models are able to learn the long-range, complicated and intertwined dependencies in the input signal, necessary to the prediction. For decoding, we decomposed each word of a large vocabulary into the set of constituent bigrams, and apply a simple cosine similarity measure between this representation and the bagged RNN prediction to retrieve the vocabulary word. We compare this method to standard word recognition techniques based on sequential character recognition. Experiments are carried out on two public databases of handwritten words (Rimes and IAM). The bigram decoder results with our bigram decoder are comparable to more conventional decoding methods based on sequences of letters.","PeriodicalId":433676,"journal":{"name":"2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124794633","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: 3

Extraction of Ancient Map Contents Using Trees of Connected Components 利用连通组件树提取古代地图内容

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-09 DOI: 10.1109/ICDAR.2017.249

Jordan Drapeau, T. Géraud, Mickaël Coustaty, J. Chazalon, J. Burie, V. Eglin, S. Bres

引用次数: 3

Bootstrapping Samples of Accidentals in Dense Piano Scores for CNN-Based Detection 基于cnn的密集钢琴乐谱中意外样本的自举检测

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2017-11-09 DOI: 10.1109/ICDAR.2017.257

Kwon-Young Choi, Bertrand Coüasnon, Y. Ricquebourg, R. Zanibbi

引用次数: 8