Learning and Adaptation for Improving Handwritten Character Recognizers

Tewari, Naveen; Namboodiri, Anoop

doi:10.1109/icdar.2009.212

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…where Gðx; iÞ can be either (15) or (19), which can be viewed as a distance metric between x and class i. The top-1 output is then defined as the distance between x and its predicted classŷ, while the top-2 output is the distance from the most competing class:…”

Section: Confidence Setupmentioning

confidence: 99%

“…For SVM adaptation, Kienzle and Chellapilla applied a biased regularization (a tradeoff between the new classifier and the original one) to retrain the classifier using the new writer-specific data [14]. Tewari and Namboodiri proposed an SVM-based multiple kernel learning method where the support vectors were adapted to better model the decision boundary of a specific writer [15]. For prototype classifier adaptation, Vuori and Korkeakoulu proposed three methods: adding new prototypes, reshaping existing prototypes, and inactivating poorly performing prototypes [16].…”

Section: Introductionmentioning

confidence: 99%

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Writer Adaptation with Style Transfer Mapping

Zhang

Liu

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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Adapting a writer-independent classifier toward the unique handwriting style of a particular writer has the potential to significantly increase accuracy for personalized handwriting recognition. This paper proposes a novel framework of style transfer mapping (STM) for writer adaptation. The STM is a writer-specific class-independent feature transformation which has a closed-form solution. After style transfer mapping, the data of different writers are projected onto a style-free space, where the writer-independent classifier needs no change to classify the transformed data and can achieve significantly higher accuracy. The framework of STM can be combined with different types of classifiers for supervised, unsupervised, and semi-supervised adaptation, where writer-specific data can be either labeled or unlabeled and need not cover all classes. In this paper, we combine STM with the state-of-the-art classifiers for large-category Chinese handwriting recognition: learning vector quantization (LVQ) and modified quadratic discriminant function (MQDF). Experiments on the online Chinese handwriting database CASIA-OLHWDB demonstrate that STM-based adaptation is very efficient and effective in improving classification accuracy. Semi-supervised adaptation achieves the best performance, while unsupervised adaptation is even better than supervised adaptation. On handwritten text data, semi-supervised adaptation achieves error reduction rates 31.95 and 25.00 percent by LVQ and MQDF, respectively.

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Section: Confidence Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Writer Adaptation with Style Transfer Mapping

Zhang

Liu

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Also, we mention those which are based on HMM [18] and support vector machines like the system described in [19] where an adaptation was realized by re-learning the different SVMs using virtual examples. Add to that, the system in [20] applied an SVM based multiple kernel learning where support vectors were adapted to better model the decision boundary of a specific writer. Furthermore, we hold up systems which use adaptation methods without modifying the writer-independent system.…”

Section: Introductionmentioning

confidence: 99%

Improved Neural Based Writer Adaptation for On-Line Recognition Systems

Haddad

Hamdani

Alimi

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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The adaptation module is a Radial Basis Function Neural Network (RBF-NN) that can be connected to the output of any recognition system and its aim is to examine the output of the writer-independent system and produce a more correct output vector close to the desired response. The proposed adaptation module is built using an incremental training named GA-AM algorithm (Growing-Adjustment Adaptation Module). Two adaptation strategies are applied : Growing and Adjustment. The growing criteria are based on the estimation of the significance of the new input and the significance of the nearest unit compared to the input. The adjustment consists of the update of two specific units (nearest and desired contributor) parameters using the standard LMS gradient descent to decrease the error at each time no new unit is allocated. This new training algorithm is evaluated by the adaptation of two handwriting recognition systems. The results, reported according to the cumulative error, show that the GA-AM algorithm leads to decreasing the classification error and to instantly adapting the recognition system to a specific user's handwriting. Performance comparison of GA-AM training algorithm with two other adaptation strategies, based on four writer-dependent datasets, are presented.

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“…There have been great researches on the field of writer adaptation which can be classified in three groups: systems reorganizing the prototypes of the database (addition, modification and deletion) [10], [11], [12], systems updating the parameters of the recognition system [15], [14], [13], [16], [6] and systems adapting without modifying the classifier's parameters [7], [8], [1], [5].…”

Section: Introductionmentioning

confidence: 99%

OHRS-MEWA: On-Line Handwriting Recognition System with Multi-environment Writer Adaptation

Haddad

Hamdani

Alimi

2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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The writer adaptation arisen with the appearance and the excessive use of Handheld devices. These devices are conceived to be used in diverse user settings which can be stationary or mobile. Most of the works tackle the writer adaptation in the "sitting at a desk" environment, nevertheless we notice a lack of contributions in the multi-environment context. In this paper we present a multi-environment writer adaptation technique to improve accuracy of writer-independent recognition system. Our system is based on adaptation module (AM) which can greatly decrease error accuracy without changing the writer-independent system. The (AM) is built using IGA-AM which is an incremental learning algorithm. First, we test the performance of the IGA-AM on Laviola dataset against GA-AM algorithm for writer adaptation. Second, we test the recognition accuracy by taking into account the writing style change proportionally to environment changes. Thus the system contains as much adaptation module as handled environments. In this paper we consider two stationary environments that are sitting at a desk and standing. Finally, results on multienvironment dataset (REGIM-MEnv) are presented.

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Learning and Adaptation for Improving Handwritten Character Recognizers

Abstract: Abstract

Cited by 6 publications

References 11 publications

Writer Adaptation with Style Transfer Mapping

Writer Adaptation with Style Transfer Mapping

Improved Neural Based Writer Adaptation for On-Line Recognition Systems

OHRS-MEWA: On-Line Handwriting Recognition System with Multi-environment Writer Adaptation

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