2019 International Conference on Document Analysis and Recognition (ICDAR)最新文献_第10页

Amharic Text Image Recognition: Database, Algorithm, and Analysis 阿姆哈拉文字图像识别:数据库、算法和分析

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00205

B. Belay, T. Habtegebrial, M. Liwicki, Gebeyehu Belay, D. Stricker

{"title":"Amharic Text Image Recognition: Database, Algorithm, and Analysis","authors":"B. Belay, T. Habtegebrial, M. Liwicki, Gebeyehu Belay, D. Stricker","doi":"10.1109/ICDAR.2019.00205","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00205","url":null,"abstract":"This paper introduces a dataset for an exotic, but very interesting script, Amharic. Amharic follows a unique syllabic writing system which uses 33 consonant characters with their 7 vowels variants of each. Some labialized characters derived by adding diacritical marks on consonants and or removing part of it. These associated diacritics on consonant characters are relatively smaller in size and challenging to distinguish the derived (vowel and labialized) characters. In this paper we tackle the problem of Amharic text-line image recognition. In this work, we propose a recurrent neural network based method to recognize Amharic text-line images. The proposed method uses Long Short Term Memory (LSTM) networks together with CTC (Connectionist Temporal Classification). Furthermore, in order to overcome the lack of annotated data, we introduce a new dataset that contains 337,332 Amharic text-line images which is made freely available at http://www.dfki.uni-kl.de/~belay/. The performance of the proposed Amharic OCR model is tested by both printed and synthetically generated datasets, and promising results are obtained.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131813089","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: 14

ICDAR 2019 Time-Quality Binarization Competition ICDAR 2019时间质量二值化竞赛

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00248

R. Lins, E. Kavallieratou, E. B. Smith, R. Bernardino, D. Jesus

引用次数: 14

DECO: A Dataset of Annotated Spreadsheets for Layout and Table Recognition DECO:用于布局和表识别的注释电子表格数据集

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00207

Elvis Koci, Maik Thiele, Josephine Rehak, Oscar Romero, Wolfgang Lehner

引用次数: 16

A Text-Context-Aware CNN Network for Multi-oriented and Multi-language Scene Text Detection 面向多语言场景文本检测的文本-上下文感知CNN网络

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00116

Yao Xiao, Minglong Xue, Tong Lu, Yirui Wu, P. Shivakumara

{"title":"A Text-Context-Aware CNN Network for Multi-oriented and Multi-language Scene Text Detection","authors":"Yao Xiao, Minglong Xue, Tong Lu, Yirui Wu, P. Shivakumara","doi":"10.1109/ICDAR.2019.00116","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00116","url":null,"abstract":"The existing deep learning based state-of-theart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitraryorientation and large aspect ratios of scene texts based on instance segmentation algorithms. However, due to the lack of context information with consideration of scene text unique characteristics, directly applying instance segmentation to text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a novel text-context-aware scene text detection CNN structure, which appropriately encodes channel and spatial attention information to construct context-aware and discriminative feature map for multi-oriented and multi-language text detection tasks. With high representation ability of textcontext-aware feature map, the proposed instance segmentation based method can not only robustly detect multi-oriented and multi-language text from natural scene images, but also produce better text detection results by greatly reducing false positives. Experiments on ICDAR2015 and ICDAR2017-MLT datasets show that the proposed method has achieved superior performances in precision, recall and F-measure than most of the existing studies.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128283920","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: 5

A Novel Procedure to Speed up the Transcription of Historical Handwritten Documents by Interleaving Keyword Spotting and user Validation 一种基于交错关键字识别和用户验证的加速历史手写文档转录的新方法

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00198

Adolfo Santoro, A. Marcelli

{"title":"A Novel Procedure to Speed up the Transcription of Historical Handwritten Documents by Interleaving Keyword Spotting and user Validation","authors":"Adolfo Santoro, A. Marcelli","doi":"10.1109/ICDAR.2019.00198","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00198","url":null,"abstract":"We propose a novel procedure to speed-up the content transcription of handwritten documents in digital historical archives when a keyword spotting system is used for the purpose. Instead of performing the validation of the system outputs in a single step, as it is customary, the proposed methodology envisaged a multi-step validation process to be embedded into a human-in-the-loop approach. At each step, the system outputs are validated and, whenever an image word that does not correspond to any entry of the keyword list is mistakenly returned by the system, its correct transcription is entered and used to query the system in the next step. The performance of our approach has been experimentally evaluated in terms of the total time to achieve the complete transcription of a subset of documents from the Bentham dataset. The results confirm that interleaving keyword spotting by the system and validation by the user leads to a significant reduction of the time required to transcribe the document content with respect to both the manual transcription and the traditional end-of-the-loop validation process.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134086431","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

Hiding Security Feature Into Text Content for Securing Documents Using Generated Font 将安全功能隐藏到文本内容中以使用生成的字体保护文档

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00196

Vinh Loc Cu, J. Burie, J. Ogier, Cheng-Lin Liu

{"title":"Hiding Security Feature Into Text Content for Securing Documents Using Generated Font","authors":"Vinh Loc Cu, J. Burie, J. Ogier, Cheng-Lin Liu","doi":"10.1109/ICDAR.2019.00196","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00196","url":null,"abstract":"Motivated by increasing possibility of the tampering of genuine documents during a transmission over digital channels, we focus on developing a watermarking framework for determining whether a given document is genuine or falsified. The proposed framework is performed by hiding a security feature or secret information within the document. In order to hide the security feature, we replace the appropriate characters of legal document by the equivalent characters coming from generated fonts, called hereafter the variations of characters. These variations are produced by training generative adversarial networks (GAN) with the features of character's skeleton and normal shape. Regarding the process of detecting hidden information, we make use of fully convolutional networks (FCN) to produce salient regions from the watermarked document. The salient regions mark positions of document where the characters are substituted by their variations, and these positions are used as a reference for extracting the hidden information. Lastly, we demonstrate that our approach gives high precision of data detection, and competitive performance compared to state-of-the-art approaches.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134109919","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

ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling - RRC-LSVT ICDAR 2019大尺度街景文本部分标注竞赛- RRC-LSVT

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/icdar.2019.00250

Yipeng Sun, Zihan Ni, Chee-Kheng Chng, Yuliang Liu, Canjie Luo, Chun Chet Ng, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin

{"title":"ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling - RRC-LSVT","authors":"Yipeng Sun, Zihan Ni, Chee-Kheng Chng, Yuliang Liu, Canjie Luo, Chun Chet Ng, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin","doi":"10.1109/icdar.2019.00250","DOIUrl":"https://doi.org/10.1109/icdar.2019.00250","url":null,"abstract":"Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and in-efficient to obtain. To scale up the amount of training data while keeping the labeling procedure cost-effective, this competition introduces a new challenge on Large-scale Street View Text with Partial Labeling (LSVT), providing 5,0000 and 400,000 images in full and weak annotations, respectively. This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing gaps between research benchmarks and real applications. During the competition period, a total number of 41 teams participate in the two tasks with 132 valid submissions, i.e., text detection and end-to-end text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of ICDAR 2019-LSVT challenge.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133219525","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: 79

Welcome Message from the Program Chairs 节目主持人的欢迎辞

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/icdar.2019.00007

G. Feigin

引用次数: 0

Handwriting Recognition Based on Temporal Order Restored by the End-to-End System 基于端到端系统时间顺序还原的手写识别

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00199

Besma Rabhi, A. Elbaati, Y. Hamdi, A. Alimi

引用次数: 12

WiSe — Slide Segmentation in the Wild 明智的幻灯片分割在野外

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00062

Monica Haurilet, Alina Roitberg, Manuel Martínez, R. Stiefelhagen

{"title":"WiSe — Slide Segmentation in the Wild","authors":"Monica Haurilet, Alina Roitberg, Manuel Martínez, R. Stiefelhagen","doi":"10.1109/ICDAR.2019.00062","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00062","url":null,"abstract":"We address the task of segmenting presentation slides, where the examined page was captured as a live photo during lectures. Slides are important document types used as visual components accompanying presentations in a variety of fields ranging from education to business. However, automatic analysis of presentation slides has not been researched sufficiently, and, so far, only preprocessed images of already digitalized slide documents were considered. We aim to introduce the task of analyzing unconstrained photos of slides taken during lectures and present a novel dataset for Page Segmentation with slides captured in the Wild (WiSe). Our dataset covers pixel-wise annotations of 25 classes on 1300 pages, allowing overlapping regions (i.e., multi-class assignments). To evaluate the performance, we define multiple benchmark metrics and baseline methods for our dataset. We further implement two different deep neural network approaches previously used for segmenting natural images and adopt them for the task. Our evaluation results demonstrate the effectiveness of the deep learning-based methods, surpassing the baseline methods by over 30%. To foster further research of slide analysis in unconstrained photos, we make the WiSe dataset publicly available to the community.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270756","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: 8