2019 International Conference on Document Analysis and Recognition (ICDAR)最新文献_第3页

Care Label Recognition 护理标签识别

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00158

Jiri Kralicek, Jiri Matas, M. Busta

{"title":"Care Label Recognition","authors":"Jiri Kralicek, Jiri Matas, M. Busta","doi":"10.1109/ICDAR.2019.00158","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00158","url":null,"abstract":"The paper introduces the problem of care label recognition and presents a method addressing it. A care label, also called a care tag, is a small piece of cloth or paper attached to a garment providing instructions for its maintenance and information about e.g. the material and size. The informationand instructions are written as symbols or plain text. Care label recognition is a challenging text and pictogram recognition problem - the often sewn text is small, looking as if printed using a non-standard font; the contrast of the text gradually fades, making OCR progressively more difficult. On the other hand, the information provided is typically redundant and thus it facilitates semi-supervised learning. The presented care label recognition method is based on the recently published End-to-End Method for Multi-LanguageScene Text, E2E-MLT, Busta et al. 2018, exploiting specific constraints, e.g. a care label vocabulary with multi-language equivalences. Experiments conducted on a newly-created dataset of 63 care label images show that even when exploiting problem-specific constraints, a state-of-the-art scene text detection and recognition method achieve precision and recall slightly above 0.6, confirming the challenging nature of the problem.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"2 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":"134355075","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

Welcome Message from the Honorary Chair 名誉主席致欢迎辞

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/icdar.2019.00005

H. Makino

引用次数: 0

Hybrid DBLSTM-SVM Based Beta-Elliptic-CNN Models for Online Arabic Characters Recognition 基于DBLSTM-SVM的β -椭圆- cnn混合模型在线识别阿拉伯字符

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00093

Y. Hamdi, H. Boubaker, Thameur Dhieb, A. Elbaati, A. Alimi

{"title":"Hybrid DBLSTM-SVM Based Beta-Elliptic-CNN Models for Online Arabic Characters Recognition","authors":"Y. Hamdi, H. Boubaker, Thameur Dhieb, A. Elbaati, A. Alimi","doi":"10.1109/ICDAR.2019.00093","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00093","url":null,"abstract":"The deep learning-based approaches have proven highly successful in handwriting recognition which represents a challenging task that satisfies its increasingly broad application in mobile devices. Recently, several research initiatives in the area of pattern recognition studies have been introduced. The challenge is more earnest for Arabic scripts due to the inherent cursiveness of their characters, the existence of several groups of similar shape characters, large sizes of respective alphabets, etc. In this paper, we propose an online Arabic character recognition system based on hybrid Beta-Elliptic model (BEM) and convolutional neural network (CNN) feature extractor models and combining deep bidirectional long short-term memory (DBLSTM) and support vector machine (SVM) classifiers. First, we use the extracted online and offline features to make the classification and compare the performance of single classifiers. Second, we proceed by combining the two types of feature-based systems using different combination methods to enhance the global system discriminating power. We have evaluated our system using LMCA and Online-KHATT databases. The obtained recognition rate is in a maximum of 95.48% and 91.55% for the individual systems using the two databases respectively. The combination of the on-line and off-line systems allows improving the accuracy rate to 99.11% and 93.98% using the same databases which exceed the best result for other state-of-the-art systems.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"134 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":"131800897","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: 15

Data Augmentation via Adversarial Networks for Optical Character Recognition/Conference Submissions 通过对抗性网络进行光学字符识别的数据增强/会议提交

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00038

Victor Storchan

引用次数: 3

Content Extraction from Lecture Video via Speaker Action Classification Based on Pose Information 基于姿态信息的演讲者动作分类的演讲视频内容提取

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00171

Fei Xu, Kenny Davila, S. Setlur, V. Govindaraju

{"title":"Content Extraction from Lecture Video via Speaker Action Classification Based on Pose Information","authors":"Fei Xu, Kenny Davila, S. Setlur, V. Govindaraju","doi":"10.1109/ICDAR.2019.00171","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00171","url":null,"abstract":"Online lecture videos are increasingly important e-learning materials for students. Automated content extraction from lecture videos facilitates information retrieval applications that improve access to the lecture material. A significant number of lecture videos include the speaker in the image. Speakers perform various semantically meaningful actions during the process of teaching. Among all the movements of the speaker, key actions such as writing or erasing potentially indicate important features directly related to the lecture content. In this paper, we present a methodology for lecture video content extraction using the speaker actions. Each lecture video is divided into small temporal units called action segments. Using a pose estimator, body and hands skeleton data are extracted and used to compute motion-based features describing each action segment. Then, the dominant speaker action of each of these segments is classified using Random forests and the motion-based features. With the temporal and spatial range of these actions, we implement an alternative way to draw key-frames of handwritten content from the video. In addition, for our fixed camera videos, we also use the skeleton data to compute a mask of the speaker writing locations for the subtraction of the background noise from the binarized key-frames. Our method has been tested on a publicly available lecture video dataset, and it shows reasonable recall and precision results, with a very good compression ratio which is better than previous methods based on content analysis.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"3 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":"115481638","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: 6

A Quality and Time Assessment of Binarization Algorithms 二值化算法的质量和时间评价

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00232

R. Lins, R. Bernardino, D. Jesus

引用次数: 14

DICE: Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis DICE: Twitter情感分析的深度智能上下文嵌入

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00157

Usman Naseem, Katarzyna Musial

引用次数: 23

Synthesis of Handwriting Dynamics using Sinusoidal Model 用正弦模型合成手写动力学

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00144

Himakshi Choudhury, S. Prasanna

{"title":"Synthesis of Handwriting Dynamics using Sinusoidal Model","authors":"Himakshi Choudhury, S. Prasanna","doi":"10.1109/ICDAR.2019.00144","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00144","url":null,"abstract":"Handwriting production is a complex mechanism of fine motor control, associated with mainly two degrees of freedom in the horizontal and vertical directions. The relation between the horizontal and vertical velocities depends on the trajectory shape and its length. In this work, we explore the generation of handwriting velocities using two sinusoidal oscillations. The proposed method follows the motor equivalence theory and considers that the patterns are stored in the form of a sequence of corner shapes and its relative location in the letter. These points are referred to as the modulation points, where the parameters of the sinusoidal oscillations are modulated to generate required velocity profiles. Depending on the location and shape of the corners, the amplitude, phase, and frequency relations between the two underlying oscillations are changed. Accordingly, this paper presents an efficient method to synthesize the velocity profiles and hence the handwriting. Further, the shape variability in the synthesized data can also be introduced by modifying the position of the modulation points and its corner shapes. The quality of the synthesized handwriting is evaluated using both subjective and quantitative evaluation methods.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"30 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":"114695295","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

A Robust Hybrid Approach for Textual Document Classification 文本文档分类的鲁棒混合方法

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00224

M. Asim, Muhammad Usman Ghani Khan, M. I. Malik, A. Dengel, Sheraz Ahmed

{"title":"A Robust Hybrid Approach for Textual Document Classification","authors":"M. Asim, Muhammad Usman Ghani Khan, M. I. Malik, A. Dengel, Sheraz Ahmed","doi":"10.1109/ICDAR.2019.00224","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00224","url":null,"abstract":"Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This although improved the overall classification accuracy, the classifiers still faced sparsity problem due to lack of better data representation techniques. Deep learning based text document classification, on the other hand, benefitted greatly from the invention of word embeddings that have solved the sparsity problem and researchers focus mainly remained on the development of deep architectures. Deeper architectures, however, learn some redundant features that limit the performance of deep learning based solutions. In this paper, we propose a two stage text document classification methodology which combines traditional feature engineering with automatic feature engineering (using deep learning). The proposed methodology comprises a filter based feature selection (FSE) algorithm followed by a deep convolutional neural network. This methodology is evaluated on the two most commonly used public datasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7.7% on 20 Newsgroups and 6.6% on BBC news datasets.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"30 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":"116009379","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

Capturing Micro Deformations from Pooling Layers for Offline Signature Verification 从池化层捕获微变形用于离线签名验证

2019 International Conference on Document Analysis and Recognition (ICDAR) Pub Date : 2019-09-01 DOI: 10.1109/ICDAR.2019.00180

Yuchen Zheng, W. Ohyama, Brian Kenji Iwana, S. Uchida

引用次数: 5