IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard

Liwicki, Marcus; Bunke, Horst

doi:10.1109/icdar.2005.132

Cited by 159 publications

(103 citation statements)

References 20 publications

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“…The task in this section is online handwriting recognition, using the IAMOnDB handwriting database (Liwicki and Bunke, 2005b) 1 . In online handwriting recognition, the state and position of the pen is recorded during writing, and used as input to the learning algorithm.…”

Section: Online Handwriting Recognitionmentioning

confidence: 99%

Supervised Sequence Labelling with Recurrent Neural Networks

Graves

2012

Studies in Computational Intelligence

2,349

2,088

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Recurrent neural networks are powerful sequence learners. They are able to incorporate context information in a flexible way, and are robust to localised distortions of the input data. These properties make them well suited to sequence labelling, where input sequences are transcribed with streams of labels. Long short-term memory is an especially promising recurrent architecture, able to bridge long time delays between relevant input and output events, and thereby access long range context. The aim of this thesis is to advance the state-of-the-art in supervised sequence labelling with recurrent networks in general, and long short-term memory in particular. Its two main contributions are (1) a new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and (2) an extension of long short-term memory to multidimensional data, such as images and video sequences. Experimental results are presented on speech recognition, online and offline handwriting recognition, keyword spotting, image segmentation and image classification, demonstrating the advantages of advanced recurrent networks over other sequential algorithms, such as hidden Markov Models.ii

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Section: Online Handwriting Recognitionmentioning

confidence: 99%

Supervised Sequence Labelling with Recurrent Neural Networks

Graves

2012

Studies in Computational Intelligence

2,349

2,088

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“…For the online experiments we used the IAM-OnDB, a database acquired from a 'smart' whiteboard [49]. The database was divided into four disjoint sets, as defined by the IAM-OnDB-t2 benchmark task: a training set containing 5,364 lines; a first validation set containing 1,438 lines; a second validation set containing 1,518 lines which can be used, for example, to optimise a language model; and a test set containing 3,859 lines.…”

Section: A Data Setsmentioning

confidence: 99%

A Novel Connectionist System for Unconstrained Handwriting Recognition

Graves¹,

Liwicki

Fernández

et al. 2009

IEEE Trans. Pattern Anal. Mach. Intell.

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1,679

887

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Abstract-Recognising lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognisers. Most recent progress in the field has been made either through improved preprocessing, or through advances in language modelling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labelling tasks where the data is hard to segment and contains long range, bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7% on online data and 74.1% on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyse its use of context. Lastly we provide an in depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.

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“…In the experiments, we use the IAM On-line English Handwritten Text Database(IAM-OnDB) [13]. IAM-OnDB consists of on-line data acquired from a white-board.…”

Section: Methodsmentioning

confidence: 99%

Text-Independent Online Writer Identification Using Hidden Markov Models

Lü

Zhang

2017

IEICE Trans. Inf. & Syst.

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SUMMARY In text-independent online writer identification, the Gaussian Mixture Model(GMM) writer model trained with the GMM-Universal Background Model(GMM-UBM) framework has acquired excellent performance. However, the system assumes the items in the observation sequence are independent, which neglects the dynamic information between observations. This work shows that although in the text-independent application, the dynamic information between observations is still important for writer identification. In order to extend the GMM-UBM system to use the dynamic information, the hidden Markov model(HMM) with Gaussian observation model is used to model each writer's handwriting in this paper and a new training schematic is proposed. In particular, the observation model parameters of the writer specific HMM are set with the Gaussian component parameters of the GMM writer model trained with the GMM-UBM framework and the state transition matrix parameters are learned from the writer specific data. Experiments show that incorporating the dynamic information is capable of improving the performance of the GMM-based system and the proposed training method is effective for learning the HMM writer model.

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IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard

Cited by 159 publications

References 20 publications

Supervised Sequence Labelling with Recurrent Neural Networks

Supervised Sequence Labelling with Recurrent Neural Networks

A Novel Connectionist System for Unconstrained Handwriting Recognition

Text-Independent Online Writer Identification Using Hidden Markov Models

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