Writer Adaptive Training and Writing Variant Model Refinement for Offline Arabic Handwriting Recognition

Dreuw, Philippe; Rybach, David; Gollan, Christian; Ney, Hermann

doi:10.1109/icdar.2009.9

Cited by 32 publications

(21 citation statements)

References 10 publications

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“…The final feature vector of size 24 is augmented by original moment values [21]. The system is writer adaptive using CMLLR for feature transformation [22]. For classification we use an HMM model with 6 segments per character.…”

Section: System Overviewmentioning

confidence: 99%

Open vocabulary handwriting recognition using combined word-level and character-level language models

Kozielski

Rybach

Hahn

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we present a unified search strategy for open vocabulary handwriting recognition using weighted finite state transducers. Additionally to a standard word-level language model we introduce a separate n-gram character-level language model for out-of-vocabulary word detection and recognition. The probabilities assigned by those two models are combined into one Bayes decision rule. We evaluate the proposed method on the IAM database of English handwriting. An improvement from 22.2% word error rate to 17.3% is achieved comparing to the closed-vocabulary scenario and the best published result.

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Section: System Overviewmentioning

confidence: 99%

Open vocabulary handwriting recognition using combined word-level and character-level language models

Kozielski

Rybach

Hahn

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The size of the Arabic characters are very different. The number of HMM states is therefore estimated using the so called Model Length Estimation (MLE) presented in [12]. The characters are divided into MLE labels with one Gaussian for each them.…”

Section: B Visual Modelmentioning

confidence: 99%

Improvement of Context Dependent Modeling for Arabic Handwriting Recognition

Hamdani

Doetsch

Ney

2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Abstract-This paper proposes the improvement of context dependent modeling for Arabic handwriting recognition. Since the number of parameters in context dependent models is huge, CART trees are used for state tying. This work is based on a new set of questions for the CART tree construction based on a "lossy mapping" categorization of the Arabic shapes. The used system is a combination of Hidden Markov Models and Recurrent Neural Networks using the hybrid approach. A comparison between a Neural network trained using the baseline labels and another one based on the CART tree labels is done. The experimental results show that the use of the CART labels for the Neural Network training beneficial. The lossy mapping based CART tree performed better than the baseline system. An absolute improvement of 2.9% in terms of Word Error Rate is performed on the test set of the OpenHaRT database.

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“…In the following experiments, we additionally use a glyph dependent model length estimation (GDL) as described in [7,8], resulting in an ML trained baseline model with 637 mixtures and 48k densities (cf. Section 2.2).…”

Section: First Pass Decodingmentioning

confidence: 99%

“…maximum-likelihood (ML) training criterion and a lexicon with multiple writing variants as proposed in [7,8]. Each character is modeled by a multi-state left-toright HMM with skip transitions and separate Gaussian mixture models (GMMs).…”

Section: Visual Modelingmentioning

confidence: 99%

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Confidence- and margin-based MMI/MPE discriminative training for off-line handwriting recognition

Dreuw

Heigold

Ney

2011

IJDAR

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We present a novel confidence-and marginbased discriminative training approach for model adaptation of a hidden Markov model (HMM) based handwriting recognition system to handle different handwriting styles and their variations.Most current approaches are maximum-likelihood (ML) trained HMM systems and try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer specific data. Here, discriminative training based on the maximum mutual information (MMI) and minimum phone error (MPE) criteria are used to train writer independent handwriting models. For model adaptation during decoding, an unsupervised confidence-based discriminative training on a word and frame level within a two-pass decoding process is proposed.The proposed methods are evaluated for closedvocabulary isolated handwritten word recognition on the IFN/ENIT Arabic handwriting database, where the word-error-rate is decreased by 33% relative compared to a ML trained baseline system. On the largevocabulary line recognition task of the IAM English handwriting database, the word-error-rate is decreased by 25% relative.

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Writer Adaptive Training and Writing Variant Model Refinement for Offline Arabic Handwriting Recognition

Cited by 32 publications

References 10 publications

Open vocabulary handwriting recognition using combined word-level and character-level language models

Open vocabulary handwriting recognition using combined word-level and character-level language models

Improvement of Context Dependent Modeling for Arabic Handwriting Recognition

Confidence- and margin-based MMI/MPE discriminative training for off-line handwriting recognition

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