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

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Effective Random-Impostor Training for Combined Segmentation Signature Verification 组合分割签名验证的有效随机冒名顶替训练
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0096
Keigo Matsuda, W. Ohyama, T. Wakabayashi, F. Kimura
{"title":"Effective Random-Impostor Training for Combined Segmentation Signature Verification","authors":"Keigo Matsuda, W. Ohyama, T. Wakabayashi, F. Kimura","doi":"10.1109/ICFHR.2016.0096","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0096","url":null,"abstract":"In this paper, we propose an improvement to the method of combined segmentation verification for multi-script signature verification. In our previous paper, we proposed generalized segmentation verification (GSV) for multi-script signature verification and evaluated the method using the SigComp dataset. GSV improved the performance of multi-script signature verification by introducing a two-stage strategy in which, during the second stage, the support vector machine (SVM) evaluated matching scores that were derived by signature verifiers during the first stage. For this strategy, the SVM was trained using a dataset that consisted of genuine and skilled-forgery verification scores calculated from signatures of third persons, whose signatures were not registered in the system. However, it was difficult to prepare skilled-forgery signatures even though the method required third-person signatures. Our proposed multi-script signature verification method uses a training dataset that contains no skilled-forgery signatures. This method uses the genuine signatures of third persons as training samples of the forgery class for SVM training. We also introduce an effective sampling method that uses a one-class SVM to reduce the sample number for the training dataset. The results of evaluation experiments using the SigComp multi-script signature dataset show that the performance of the proposed method is competitive with that of the method trained with a skilled-forgery dataset for multi-script signature verification.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"2 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":"129493659","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
Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition 基于注意力的端到端离线手写识别的双向解码器网络
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0074
P. Doetsch, Albert Zeyer, H. Ney
{"title":"Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition","authors":"P. Doetsch, Albert Zeyer, H. Ney","doi":"10.1109/ICFHR.2016.0074","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0074","url":null,"abstract":"Recurrent neural networks that can be trained end-to-end on sequence learning tasks provide promising benefits over traditional recognition systems. In this paper, we demonstrate the application of an attention-based long short-term memory decoder network for offline handwriting recognition and analyze the segmentation, classification and decoding errors produced by the model. We further extend the decoding network by a bidirectional topology together with an integrated length estimation procedure and show that it is superior to unidirectional decoder networks. Results are presented for the word and text line recognition tasks of the RIMES handwriting recognition database. The software used in the experiments is freely available for academic research purposes.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"33 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":"129968352","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: 23
Tied Spatial Transformer Networks for Digit Recognition 用于数字识别的绑定空间变压器网络
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0102
B. Cirstea, Laurence Likforman-Sulem
{"title":"Tied Spatial Transformer Networks for Digit Recognition","authors":"B. Cirstea, Laurence Likforman-Sulem","doi":"10.1109/ICFHR.2016.0102","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0102","url":null,"abstract":"This paper reports a new approach based on convolutional neural networks (CNNs), which uses spatial transformer networks (STNs). The approach, referred to as Tied Spatial Transformer Networks (TSTNs), consists of training a system which combines a localization CNN and a classification CNN whose weights are shared. The localization CNN is used for predicting an affine transform for the input image, which is then processed according to the predicted parameters and passed through the classification CNN. We have conducted initial experiments on the cluttered MNIST dataset of noisy digits, comparing the TSTN and STN with identical configurations of trainable parameters, but untied, as well as the classification CNN only, applied to the unprocessed images. In all these cases, we obtain better results using the TSTN. We conjecture that the TSTN provides a regularization effect, as compared to untied STNs. Further experiments seem to support this hypothesis.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"763 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":"134132680","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
A Connection Reduced Network for Similar Handwritten Chinese Character Discrimination 相似手写体汉字识别的连接减少网络
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0023
Yunxue Shao, Guanglai Gao, Chunheng Wang
{"title":"A Connection Reduced Network for Similar Handwritten Chinese Character Discrimination","authors":"Yunxue Shao, Guanglai Gao, Chunheng Wang","doi":"10.1109/ICFHR.2016.0023","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0023","url":null,"abstract":"One difficulty in handwritten Chinese character recognition (HCCR) is due to the large number of similar characters. In this study, we propose a connection reduced network (CRN) to discriminate similar pairs. Each hidden neuron in CRN is restricted to has one input signal and the strength of this input is set as a variable which is selected from the input of the network. Experimental results based on 100 similar pairs demonstrate that the proposed method yields highly competitive test recognition results compared to the state-of-the-art methods, while consuming less memory and time resources.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"1 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":"130133834","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
Semantic and Verbatim Word Spotting Using Deep Neural Networks 使用深度神经网络的语义和逐字单词识别
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0065
T. Wilkinson, Anders Brun
{"title":"Semantic and Verbatim Word Spotting Using Deep Neural Networks","authors":"T. Wilkinson, Anders Brun","doi":"10.1109/ICFHR.2016.0065","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0065","url":null,"abstract":"In the last few years, deep convolutional neural networks have become ubiquitous in computer vision, achieving state-of-the-art results on problems like object detection, semantic segmentation, and image captioning. However, they have not yet been widely investigated in the document analysis community. In this paper, we present a word spotting system based on convolutional neural networks. We train a network to extract a powerful image representation, which we then embed into a word embedding space. This allows us to perform word spotting using both query-by-string and query-by-example in a variety of word embedding spaces, both learned and handcrafted, for verbatim as well as semantic word spotting. Our novel approach is versatile and the evaluation shows that it outperforms the previous state-of-the-art for word spotting on standard datasets.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"4 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":"127988116","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: 78
A System for Off-Line Arabic Handwritten Word Recognition Based on Bayesian Approach 基于贝叶斯方法的离线阿拉伯语手写词识别系统
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0108
Akram Khémiri, A. Kacem, A. Belaïd, M. Elloumi
{"title":"A System for Off-Line Arabic Handwritten Word Recognition Based on Bayesian Approach","authors":"Akram Khémiri, A. Kacem, A. Belaïd, M. Elloumi","doi":"10.1109/ICFHR.2016.0108","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0108","url":null,"abstract":"In this work, a system based on a Bayesian approach, for the off-line recognition of handwritten arabic words, is proposed. Different structural features such as ascenders, descenders, loops and diacritic, are extracted from word's image, tacking into account the morphology of handwritten arabic words. For accurate features extraction, we proposed a novel method to estimate the word's baseline and evaluated it using the IFN-ENIT Tunisian city names dataset ground-truth. The extracted features are used as input to some variants of Bayesian networks, notably Naïve Bayes (NB), Tree Augmented naïve bayes Network (TAN), Horizontal and Vertical Hidden Markov Model (VH-HMM) and Dynamic Bayesian Network (DBN). Results are reported on the benchmarking IFN/ENIT which indicate the robustness and the effectiveness of the proposed approach. The best word recognition rate we obtained achieves 90.02% for the bi-stream VH-HMM.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"27 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":"123020361","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: 18
Using the MGGI Methodology for Category-Based Language Modeling in Handwritten Marriage Licenses Books 基于类别的MGGI方法在手写结婚证书语言建模中的应用
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0069
Verónica Romero, A. Fornés, E. Vidal, Joan Andreu Sánchez
{"title":"Using the MGGI Methodology for Category-Based Language Modeling in Handwritten Marriage Licenses Books","authors":"Verónica Romero, A. Fornés, E. Vidal, Joan Andreu Sánchez","doi":"10.1109/ICFHR.2016.0069","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0069","url":null,"abstract":"Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"1 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":"125724191","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 Unified French/English Syllabic Model for Handwriting Recognition 用于手写识别的统一法语/英语音节模型
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0104
Wassim Swaileh, Julien Lerouge, T. Paquet
{"title":"A Unified French/English Syllabic Model for Handwriting Recognition","authors":"Wassim Swaileh, Julien Lerouge, T. Paquet","doi":"10.1109/ICFHR.2016.0104","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0104","url":null,"abstract":"In this paper we introduce a new unified syllabic model for French and English handwriting recognition, based on hidden Markov models (HMM). The recognition system training and recognition components such as optical models, lexicons and language models are designed to be language independent. In this purpose a syllable based model is proposed for French and English. This model is evaluated and compared to n-gram character and words models. A promising performance is achieved by the syllabic model, which meets the words model performance, with the advantage of a reduced system complexity. Furthermore, the unification of likely similar scripts improves the system performance over all models considering the English and French languages. The French RIMES and the English IAM datasets are used for the evaluation.","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":"122812295","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
ICFHR2016 Competition on Handwritten Text Recognition on the READ Dataset ICFHR2016 READ数据集手写文本识别竞赛
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0120
Joan Andreu Sánchez, Verónica Romero, A. Toselli, E. Vidal
{"title":"ICFHR2016 Competition on Handwritten Text Recognition on the READ Dataset","authors":"Joan Andreu Sánchez, Verónica Romero, A. Toselli, E. Vidal","doi":"10.1109/ICFHR.2016.0120","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0120","url":null,"abstract":"This paper describes the Handwritten Text Recognition (HTR) competition on the READ dataset that has been held in the context of the International Conference on Frontiers in Handwriting Recognition 2016. This competition aims to bring together researchers working on off-line HTR and provide them a suitable benchmark to compare their techniques on the task of transcribing typical historical handwritten documents. Two tracks with different conditions on the use of training data were proposed. Ten research groups registered in the competition but finally five submitted results. The handwritten images for this competition were drawn from the German document Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805, used in the READ project. The selected dataset is written by several hands and entails significant variabilities and difficulties. The five participants achieved good results with transcriptions word error rates ranging from 21% to 47%.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"85 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":"131264529","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: 52
Class-Based Contextual Modeling for Handwritten Arabic Text Recognition 手写体阿拉伯文本识别的基于类的上下文建模
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) Pub Date : 2016-10-01 DOI: 10.1109/ICFHR.2016.0107
Irfan Ahmad, G. Fink
{"title":"Class-Based Contextual Modeling for Handwritten Arabic Text Recognition","authors":"Irfan Ahmad, G. Fink","doi":"10.1109/ICFHR.2016.0107","DOIUrl":"https://doi.org/10.1109/ICFHR.2016.0107","url":null,"abstract":"In this paper we will present our investigations related to contextual modeling for HMM-based handwritten Arabic text recognition. We will, first, discuss the justifications and the need for contextual modeling for handwritten Arabic text recognition. Next, we will discuss the issues related to contextual modeling for Arabic text recognition. Finally, we will present our novel class-based contextual modeling for HMM-based handwritten Arabic text recognition. Experiment results on word recognition tasks show improvements in word recognition rates when compared to using standard contextual HMMs. Moreover, the recognizers are significantly more compact as compared to the standard contextual HMM systems.","PeriodicalId":194844,"journal":{"name":"2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)","volume":"123 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":"133991106","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
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