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

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An HMM-based Over-Segmentation Method for Touching Chinese Handwriting Recognition 一种基于hmm的触控中文手写识别过分割方法
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0071
Liang Xu, Wei-liang Fan, Jun Sun, S. Naoi
{"title":"An HMM-based Over-Segmentation Method for Touching Chinese Handwriting Recognition","authors":"Liang Xu, Wei-liang Fan, Jun Sun, S. Naoi","doi":"10.1109/ICFHR.2016.0071","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0071","url":null,"abstract":"The segmentation of touching characters is still a challenging problem in offline Chinese handwriting recognition. One feasible solution is through the over-segmentation strategy which maintains a high recall of correct cuts between adjacent characters and a moderate level of redundant cuts within a single character. Previous redundant cut filtering methods rely on either pure heuristics or learned geometric properties of correct cuts. In this work, we extend learning based cut filtering method from single cut level to cut sequence level by Hidden Markov Model (HMM). As a stochastic sequential modeling tool, HMM can utilize not only properties of individual cuts but also the left-to-right temporal context and spatial dependencies among a sequence of neighboring cuts. The experimental results on a large touching character dataset show that the proposed method is effective for over-segmentation and gives better performance than previous methods.","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":"115255799","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
Comparison of Parsing Algorithms for Recognizing Online Handwritten Mathematical Expressions 在线手写数学表达式识别的解析算法比较
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0079
A. D. Le, M. Nakagawa
{"title":"Comparison of Parsing Algorithms for Recognizing Online Handwritten Mathematical Expressions","authors":"A. D. Le, M. Nakagawa","doi":"10.1109/ICFHR.2016.0079","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0079","url":null,"abstract":"Parsing process is the most important process in recognition of online handwritten mathematical expressions. There are two basic approaches: stroke order dependent (SOD) and stroke order free (SOF) approaches. The SOD approach depends on stroke order while the SOF approach is free from stroke order. Although both approaches have shown high recognition rates in recently competitions, there are a few of works that analyze the complexities of parsing algorithms in the same experimental conditions. In this work, we have tested and analyzed recognition rate, recognition speed and memory space required by parsing algorithms on CROHME 2014. SOF is slightly superior to SOD in recognition rate, but SOD is faster in processing time and lower in memory space than SOF.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"137 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":"124381110","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: 2
Towards the Recognition of Compound Music Notes in Handwritten Music Scores 手写体乐谱中复合音符的识别研究
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0092
Arnau Baró, Pau Riba, A. Fornés
{"title":"Towards the Recognition of Compound Music Notes in Handwritten Music Scores","authors":"Arnau Baró, Pau Riba, A. Fornés","doi":"10.1109/ICFHR.2016.0092","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0092","url":null,"abstract":"The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"36 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":"122269324","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: 21
Comparison of Zone-Features for Online Bengali and Devanagari Word Recognition Using HMM 基于HMM的孟加拉语和梵语在线词识别的区域特征比较
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0087
Rajib Ghosh, P. Roy
{"title":"Comparison of Zone-Features for Online Bengali and Devanagari Word Recognition Using HMM","authors":"Rajib Ghosh, P. Roy","doi":"10.1109/ICFHR.2016.0087","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0087","url":null,"abstract":"This paper presents a comparative study of three feature extraction approaches for online handwritten word recognition of two major Indic scripts-Bengali and Devanagari using Hidden Markov Model (HMM). First approach uses feature extraction from whole stroke without local zone division after segmenting the word into its basic strokes. Whereas, other two approaches consider the segmentation of a word into its basic strokes and a local zone wise analysis of each online stroke. Among these two zone wise local features, one takes into account structural and directional features and other uses dominant points, detected from strokes using slope angles, to find the local features. These features are studied in HMM-based word recognition platform. From the comparative study of the word recognition results, we have noted that dominant point based local feature extraction provides best accuracies for both Bengali and Devanagari scripts. We have obtained 90.23% and 93.82% accuracies for Bengali and Devanagari scripts respectively.","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":"129726568","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: 16
KPTI: Katib's Pashto Text Imagebase and Deep Learning Benchmark KPTI: Katib的普什图语文本图像库和深度学习基准
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0090
Riaz Ahmad, Muhammad Zeshan Afzal, Sheikh Faisal Rashid, M. Liwicki, T. Breuel, A. Dengel
{"title":"KPTI: Katib's Pashto Text Imagebase and Deep Learning Benchmark","authors":"Riaz Ahmad, Muhammad Zeshan Afzal, Sheikh Faisal Rashid, M. Liwicki, T. Breuel, A. Dengel","doi":"10.1109/ICFHR.2016.0090","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0090","url":null,"abstract":"This paper presents the first Pashto text image database for scientific research and thereby the first dataset with complete handwritten and printed text line images which ultimately covers all alphabets of Arabic and Persian languages. Language like Pashto, written in a complex way by calligraphers, still requires a mature Optical Character Recognition (OCR), system. Although 50 million people use this language both for oral and written communication, there is no significant effort which is devoted to the recognition of Pashto Script. A real dataset of 17,015 images having Pashto text lines is introduced. The images are acquired via scanning from hand scribed Pashto books. Further, in this work, we evaluated the performance of deep learning based models like Bidirectional and Multi-Dimensional Long Short Term Memory (BLSTM and MDLSTM) networks for Pashto texts and provide a baseline character error rate of 9.22%.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"12 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":"134008379","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: 24
Multi-feature Selection of Handwriting for Gender Identification Using Mutual Information 基于互信息的手写体性别识别多特征选择
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0111
J. Tan, Ning Bi, C. Suen, N. Nobile
{"title":"Multi-feature Selection of Handwriting for Gender Identification Using Mutual Information","authors":"J. Tan, Ning Bi, C. Suen, N. Nobile","doi":"10.1109/ICFHR.2016.0111","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0111","url":null,"abstract":"This paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features can be extracted from different methods. Therefore, the multi-feature sets are irrelevant and redundant. The conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. This paper proposes a Mutual Information (MI) approach, that focuses on feature selection. The approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. The classification is carried out using a Support Vector Machine (SVM) on two databases. The first database comes from the ICDAR 2013 competition on gender prediction, the other database contains the Registration-Document-Form (RDF) database in Chinese. The proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"25 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":"132407002","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
Gender Classification from Offline Handwriting Images Using Textural Features 基于纹理特征的离线手写图像性别分类
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0080
Ali Mirza, Momina Moetesum, I. Siddiqi, Chawki Djeddi
{"title":"Gender Classification from Offline Handwriting Images Using Textural Features","authors":"Ali Mirza, Momina Moetesum, I. Siddiqi, Chawki Djeddi","doi":"10.1109/ICFHR.2016.0080","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0080","url":null,"abstract":"Prediction of gender and other demographic attributes of individuals from handwriting samples offers an interesting basic, as well as applied research problem. The correlation between gender and the visual appearance of handwriting has been validated by a number of studies and the present study is based on the same idea. We exploit the textural measurements as the discriminating attribute between male and female writings. The textural information in a writing is captured by applying a bank of Gabor filters to the image of handwriting. The mean and standard deviation values of the filter responses are collected in matrix and the Fourier transform of the matrix is used as a feature. Classification is carried out using a feed forward neural network. The proposed technique evaluated on a subset of the QUWI database realized promising results under different experimental settings.","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":"132318641","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: 29
Automatic Signature Segmentation Using Hyper-Spectral Imaging 使用超光谱成像自动签名分割
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0017
Umair Muneer Butt, Sheraz Ahmed, F. Shafait, C. Nansen, A. Mian, M. I. Malik
{"title":"Automatic Signature Segmentation Using Hyper-Spectral Imaging","authors":"Umair Muneer Butt, Sheraz Ahmed, F. Shafait, C. Nansen, A. Mian, M. I. Malik","doi":"10.1109/ICFHR.2016.0017","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0017","url":null,"abstract":"In this paper, we propose a method for automatic signature segmentation using hyper-spectral imaging. The proposed method first uses the connected component analysis and local features to segment the printed text and signatures. Secondly, it uses spectral response of text, signature, and background to extract signature pixels. The proposed method is robust, and remains unaffected by color and intensity of the ink, and by any structural information of the text, as the classification relies exclusively on the spectral response of the document. The proposed method can extract signature pixels either overlapping or non-overlapping from different backgrounds like, logos, tables, stamps, and printed text. We used high-resolution hyper-spectral imaging to study and classify 300 documents with varying backgrounds. We evaluated the proposed classification method and compared results with the state-of-the art system. The proposed method outperformed the state-of-the-art system and achieved 100% precision and 84% recall.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"26 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":"134644952","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
Learning Text-Line Localization with Shared and Local Regression Neural Networks 用共享和局部回归神经网络学习文本行定位
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0014
Bastien Moysset, J. Louradour, Christopher Kermorvant, Christian Wolf
{"title":"Learning Text-Line Localization with Shared and Local Regression Neural Networks","authors":"Bastien Moysset, J. Louradour, Christopher Kermorvant, Christian Wolf","doi":"10.1109/ICFHR.2016.0014","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0014","url":null,"abstract":"Text line detection and localisation is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a novel approach for text line localisation based on Convolutional Neural Networks and Multidimensional Long Short-Term Memory cells as a regressor in order to predict the coordinates of the text line bounding boxes directly from the pixel values. Targeting typically large images in document image analysis, we propose a new model using weight sharing over local blocks. We compare two strategies: directly predicting the four coordinates or predicting lower-left and upper-right points separately followed by matching. We evaluate our work on the highly unconstrained Maurdor dataset and show that our method outperforms both other machine learning and image processing methods.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"46 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":"124712224","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
Region Proposal for Pattern Spotting in Historical Document Images 历史文献图像模式识别的区域建议
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0075
Sovann En, C. Petitjean, Stéphane Nicolas, L. Heutte, F. Jurie
{"title":"Region Proposal for Pattern Spotting in Historical Document Images","authors":"Sovann En, C. Petitjean, Stéphane Nicolas, L. Heutte, F. Jurie","doi":"10.1109/ICFHR.2016.0075","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0075","url":null,"abstract":"Pattern spotting consists in searching in a document image for the occurrences of a queried graphical object. The main challenge in pattern spotting is that the query image is generally small and the occurrences may be located at any random places in the image. Rather than exhaustively indexing all possible subwindows extracted from the document images, the common way is to rely on a segmentation or a document layout analysis to limit the search space. However, there is no segmentation nor document layout analysis technique reliable enough for historical document images. Region proposal, a technique used to generate a set of regions potentially containing an object, has contributed to many state of the art object detection systems recently. Although it is initially proposed for object detection, we will show that region proposal also offers promising results for document images, particularly in the case of pattern spotting. In this paper, we aim at investigating the use of region proposal to produce high quality subwindows to replace the usual document layout analysis step and the blind sliding windowing step. From experiments conducted on the DocExplore dataset, we show that region proposal generates a comparable number of subwindows while helping the system to achieve significant better results than the system built with commonly used layout analysis techniques.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"2009 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":"125628033","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
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