2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)最新文献_第8页

Co-occurrence Features for Writer Identification 作者识别的共现特征

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0027

Sheng He, Lambert Schomaker

引用次数: 15

ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016) ICFHR2016手写关键词识别比赛(H-KWS 2016)

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0117

I. Pratikakis, Konstantinos Zagoris, B. Gatos, J. Puigcerver, A. Toselli, E. Vidal

引用次数: 37

Sheet Music Statistical Layout Analysis 乐谱统计布局分析

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0066

Vicente Bosch Campos, Jorge Calvo-Zaragoza, A. Toselli, Enrique Vidal-Ruiz

{"title":"Sheet Music Statistical Layout Analysis","authors":"Vicente Bosch Campos, Jorge Calvo-Zaragoza, A. Toselli, Enrique Vidal-Ruiz","doi":"10.1109/ICFHR.2016.0066","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0066","url":null,"abstract":"In order to provide access to the contents of ancient music scores to researchers, the transcripts of both the lyrics and the musical notation is required. Before attempting any type of automatic or semi-automatic transcription of sheet music, an adequate layout analysis (LA) is needed. This LA must provide not only the locations of the different image regions, but also adequate region labels to distinguish between different region types such as staff, lyric, etc. To this end, we adapt a stochastic framework for LA based on Hidden Markov Models that we had previously introduced for detection and classification of text lines in typical handwritten text images. The proposed approach takes a scanned music score image as input and, after basic preprocessing, simultaneously performs region detection and region classification in an integrated way. To assess this statistical LA approach several experiments were carried out on a representative sample of a historical music archive, under different difficulty settings. The results show that our approach is able to tackle these structured documents providing good results not only for region detection but also for classification of the different regions.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127378846","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: 28

Word Spotting Using Radial Descriptor Graph 基于径向描述符图的词定位

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0019

M. Kassis, Jihad El-Sana

引用次数: 9

Deep Knowledge Training and Heterogeneous CNN for Handwritten Chinese Text Recognition 手写体中文文本识别的深度知识训练和异构CNN

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0028

Song Wang, Li Chen, Liang Xu, Wei-liang Fan, Jun Sun, S. Naoi

引用次数: 47

Historical Manuscript Production Date Estimation Using Deep Convolutional Neural Networks 基于深度卷积神经网络的历史手稿制作日期估计

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0048

Fredrik Wahlberg, T. Wilkinson, Anders Brun

引用次数: 18

A Method of Synthesizing Handwritten Chinese Images for Data Augmentation 一种用于数据增强的手写体中文图像合成方法

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0033

Xi Shen, Ronaldo O. Messina

{"title":"A Method of Synthesizing Handwritten Chinese Images for Data Augmentation","authors":"Xi Shen, Ronaldo O. Messina","doi":"10.1109/ICFHR.2016.0033","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0033","url":null,"abstract":"The performance of printed document recognition has been significantly improved by generating synthetic images to augment the training data, particularly by providing more variability in the linguistic contents. Handwriting recognition benefits less from this data augmentation and the only variability that is usually added is via artificially generated combinations of skew, slant and noise. Generating handwritten text is complex due to variations in form, scale and spatial placement of the characters, and can be further complicated by the cursive aspects of the script. We propose a novel strategy, in the particular case of Chinese characters, to generate synthetic lines of text, given samples of the isolated characters. The well-known CASIA database is used to train MDLSTM-RNN models and also in the creation of synthetic line images. On an independent set of real-world images, a model trained only on synthetic images achieved a small relative reduction of 4.4% in the character error rate with respect to a baseline model trained exclusively on real images, while training on a combination of real and synthetic images resulted in a appreciable reduction of 10.4%.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125516277","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

Fourier Coefficients for Fraud Handwritten Document Classification through Age Analysis 基于年龄分析的欺诈手写文档的傅里叶系数分类

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0018

R. K. Srinivas, P. Shivakumara, B. Navya, G. Pooja, Navya Prakash, G. Kumar, U. Pal, Tong Lu

{"title":"Fourier Coefficients for Fraud Handwritten Document Classification through Age Analysis","authors":"R. K. Srinivas, P. Shivakumara, B. Navya, G. Pooja, Navya Prakash, G. Kumar, U. Pal, Tong Lu","doi":"10.1109/ICFHR.2016.0018","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0018","url":null,"abstract":"As new digital technologies emerge to improve living style, at the same time, it also lead to increase crimes. Unlike existing approaches that use content of handwriting for fraud/forged document identification, in this paper we propose a novel approach that explores the quality of handwritten documents by considering both foreground and background information to identify whether it is old or new. The proposed approach works based on the fact that if a fraud document is created with some gaps after the original one, the fraud document happened to be a new one and the original happened to be an old one in this work. To identify whether a given handwritten document is old or new with gaps, we propose to divide Fourier coefficients of the input image into positive and negative coefficient images, and then reconstruct respective images to conquer two reconstructed ones. The contrast of the reconstructed images obtained before and after divide-conquer is studied to analyze the ages of the document based on image quality. The proposed approach finds a unique relationship between reconstructed images, obtained before and after divide-conquer, to identify the input image as old or new. To evaluate the proposed approach, we conduct experiments on our own handwritten dataset and a standard database, namely, Google-LIFE magazine. Comparative studies with the existing approaches show that the proposed approach outperforms the existing approaches in terms of classification rate.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"2249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130210507","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: 13

ICFHR2016 Competition on Local Attribute Detection for Handwriting Recognition ICFHR2016手写识别局部属性检测竞赛

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0119

Michael Murdock, J. Reese, S. Reid

引用次数: 3

N-Light-N: A Highly-Adaptable Java Library for Document Analysis with Convolutional Auto-Encoders and Related Architectures N-Light-N:一个具有卷积自编码器和相关架构的高适应性文档分析Java库

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0091

Mathias Seuret, R. Ingold, M. Liwicki

引用次数: 7