2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)最新文献_第10页

Offline Writer Identification Using Local Derivative Pattern 使用局部派生模式的脱机写入器识别

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0073

S. Kanetkar, Ayush Pathania, V. Venugopal, S. Sundaram

引用次数: 4

Line-of-Sight Stroke Graphs and Parzen Shape Context Features for Handwritten Math Formula Representation and Symbol Segmentation 手写数学公式表示和符号分割的视线笔画和Parzen形状上下文特征

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0044

Lei Hu, R. Zanibbi

{"title":"Line-of-Sight Stroke Graphs and Parzen Shape Context Features for Handwritten Math Formula Representation and Symbol Segmentation","authors":"Lei Hu, R. Zanibbi","doi":"10.1109/ICFHR.2016.0044","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0044","url":null,"abstract":"This paper presents a new representation for handwritten math formulae: a Line-of-Sight (LOS) graph over handwritten strokes, computed using stroke convex hulls. Experimental results using the CROHME 2012 and 2014 datasets show that LOS graphs capture the visual structure of handwritten formulae better than commonly used graphs such as Time-series, Minimum Spanning Trees, and k-Nearest Neighbor graphs. We then introduce a shape context-based feature (Parzen window Shape Contexts (PSC)) which is combined with simple geometric features and the distance in time between strokes to obtain state-of-the-art symbol segmentation results (92.43% F-measure for CROHME 2014). This result is obtained using a simple method, without use of OCR or an expression grammar. A binary random forest classifier identifies which LOS graph edges represent stroke pairs that should be merged into symbols, with connected components over merged strokes defining symbols. Line-of-Sight graphs and Parzen Shape Contexts represent visual structure well, and might be usefully applied to other notations.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"138 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":"131473923","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: 19

Online Handwritten Cursive Word Recognition by Combining Segmentation-Free and Segmentation-Based Methods 基于分词和无分词相结合的在线手写体草书识别方法

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0084

Bilan Zhu, Arti Shivram, V. Govindaraju, M. Nakagawa

引用次数: 4

On the Design of Personal Digital Bodyguards: Impact of Hardware Resolution on Handwriting Analysis 个人数字保镖的设计:硬件分辨率对笔迹分析的影响

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0043

Daniel Martín-Albo, Luis A. Leiva, R. Plamondon

引用次数: 7

Online Writer Identification Using Sparse Coding and Histogram Based Descriptors 基于稀疏编码和直方图描述符的在线作者识别

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0110

Isht Dwivedi, Swapnil Gupta, V. Venugopal, S. Sundaram

引用次数: 8

Secure Arabic Handwritten CAPTCHA Generation Using OCR Operations 安全阿拉伯手写CAPTCHA生成使用OCR操作

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0035

Suliman A. Alsuhibany, M. T. Parvez

{"title":"Secure Arabic Handwritten CAPTCHA Generation Using OCR Operations","authors":"Suliman A. Alsuhibany, M. T. Parvez","doi":"10.1109/ICFHR.2016.0035","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0035","url":null,"abstract":"Handwritten CAPTCHAs can be generated from pre-written or synthesized words, with added distortions and noise to survive OCR attacks. This paper takes a different approach for generating CAPTCHAs: use OCR operations themselves to secure the CAPTCHAs. Therefore, we utilize a number of operations found in many handwriting recognition systems (like, segmentation, baseline detection, etc.) to distort a pre-written word image itself, so that breaking the resulting CAPTCHA becomes more difficult. These OCR operations are in addition to the global image distortions that are generally done on the CAPTCHAs. The proposed method is reported for Arabic handwritten words as the cursive script of Arabic allows various OCR operations on it. To the best of our knowledge, this work is the first to generate Arabic handwritten CAPTCHAs. We evaluate our method on KHATT database of offline Arabic handwritten text. In terms of usability, we have achieved 88% to 90% accuracy. Security evaluation is done using holistic word recognition with accuracy less than 0.5%. Lexicon based attack is made difficult by working at Arabic sub-word level and then randomly selecting sub-words to build a CAPTCHA.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"78 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":"128401130","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: 11

Random Projected Convolutional Feature for Scene Text Recognition 场景文本识别中的随机投影卷积特征

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0036

Rui Wu, Shuli Yang, Dawei Leng, Zhenbo Luo, Yunhong Wang

{"title":"Random Projected Convolutional Feature for Scene Text Recognition","authors":"Rui Wu, Shuli Yang, Dawei Leng, Zhenbo Luo, Yunhong Wang","doi":"10.1109/ICFHR.2016.0036","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0036","url":null,"abstract":"Text recognition in natural scene image is an important yet challenging problem by its irregular nature. A novel method based on random projection and deep neural network(DNN) is proposed in this article. Firstly the word image is converted to multi-layers' convolutional neural network(CNN) feature sequence with sliding window. Then random projection(RP) is used to embed the original high-dimensional feature into a low-dimensional space. Finally, recurrent neural network(RNN) model is trained to recognize the text in word image based on the RP-CNN feature. The benefits of using RP is two-fold. It can preserve the geometrical relationship in dimension reduction, while reduce the computation and storage burden of the following RNN training effectively without much information loss. Moreover, RP brings information diversity with randomness which can improve the generation ability of original feature. Experiments show that recognition performance of RP-CNN feature, with 85% dimension reduction, is similar to the original high-dimensional ones. By ensemble of several RNN models based on various RP-CNN features, we obtain higher performance than single RNN based on original CNN feature. The proposed method shows competitive performance on public datasets such as SVT, ICDAR03, ICDAR13.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"24 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":"125961247","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: 11

Faster Segmentation-Free Handwritten Chinese Text Recognition with Character Decompositions 基于字符分解的快速无分割手写中文文本识别

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0103

Théodore Bluche, Ronaldo O. Messina

引用次数: 9

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-07-02 DOI: 10.1109/ICFHR.2016.0058

Brian Kenji Iwana, Volkmar Frinken, S. Uchida

引用次数: 7

Multiple Generation of Bengali Static Signatures 孟加拉语静态签名的多代

2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-07-02 DOI: 10.1109/ICFHR.2016.0021

Moisés Díaz Cabrera, S. Chanda, M. A. Ferrer-Ballester, C. Banerjee, Anirban Majumdar, C. Carmona-Duarte, Parikshit Acharya, U. Pal

引用次数: 4