{"title":"Curriculum Learning for Handwritten Text Line Recognition","authors":"J. Louradour, Christopher Kermorvant","doi":"10.1109/DAS.2014.38","DOIUrl":"https://doi.org/10.1109/DAS.2014.38","url":null,"abstract":"Recurrent Neural Networks (RNN) have recently achieved the best performance in off-line Handwriting Text Recognition. At the same time, learning RNN by gradient descent leads to slow convergence, and training times are particularly long when the training database consists of full lines of text. In this paper, we propose an easy way to accelerate stochastic gradient descent in this set-up, and in the general context of learning to recognize sequences. The principle is called Curriculum Learning, or shaping. The idea is to first learn to recognize short sequences before training on all available training sequences. Experiments on three different handwritten text databases (Rimes, IAM, OpenHaRT) show that a simple implementation of this strategy can significantly speed up the training of RNN for Text Recognition, and even significantly improve performance in some cases.","PeriodicalId":220495,"journal":{"name":"2014 11th IAPR International Workshop on Document Analysis Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115551546","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}
U. Porwal, Chetan Ramaiah, Ashish Kumar, V. Govindaraju
{"title":"Multiclass Learning for Writer Identification Using Error-Correcting Codes","authors":"U. Porwal, Chetan Ramaiah, Ashish Kumar, V. Govindaraju","doi":"10.1109/DAS.2014.73","DOIUrl":"https://doi.org/10.1109/DAS.2014.73","url":null,"abstract":"Writer Identification can be seen as a multi-class learning problem where number of writers are different classes. One of the fundamental approaches to solve a multi-class problemis by breaking it into binary classification tasks. In this work weare proposing a generic approach for multi-class classification using an ensemble of binary classifiers. We assign a distributedoutput representation to each class in the form of codewords andan ensemble of binary classifiers is created where each classifierpredicts one bit of the codeword. Actual label is determined using Belief Propagation algorithm on a graph constructed from the code matrix. We have performed experiments on a new publiclyavailable IBM-UB-1 dataset for the task of writer identification to show the efficacy of our method.","PeriodicalId":220495,"journal":{"name":"2014 11th IAPR International Workshop on Document Analysis Systems","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132368258","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}
{"title":"Sequential Word Spotting in Historical Handwritten Documents","authors":"D. F. Mota, R. Manmatha, A. Fornés, J. Lladós","doi":"10.1109/DAS.2014.18","DOIUrl":"https://doi.org/10.1109/DAS.2014.18","url":null,"abstract":"In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered index pages of family names.","PeriodicalId":220495,"journal":{"name":"2014 11th IAPR International Workshop on Document Analysis Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121502371","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}