Writer adaptation for online handwriting recognition

Connell, S.D.; Jain, Anil K.

doi:10.1109/34.990135

Cited by 78 publications

(33 citation statements)

References 23 publications

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“…(3,4) at personalization time. We learn a set or portfolio of classifiers at training time and choose the one that performs best on the personalization samples.…”

Section: Learning a Portfolio Of Classifiersmentioning

confidence: 99%

“…Typically, personalization involves learning the general classifier's parameters on the training data and then adapting these parameters to give the best performance on some "personalization" set -extra training samples collected from the intended user [11,19,21]. This type of user adaptation is used extensively in speech [12,17] and handwriting recognition [3,11], with the obvious tradeoff between the amount of personalization data (and subsequent accuracy) versus the inconvenience to the user. Depending on the extent of adaptation, the personalization step may involve a computationally expensive search for user parameters.…”

Section: Introductionmentioning

confidence: 99%

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Gesture Recognition Portfolios for Personalization

Yao

Gool

Kohli

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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Human gestures, similar to speech and handwriting, are often unique to the individual. Training a generic classifier applicable to everyone can be very difficult and as such, it has become a standard to use personalized classifiers in speech and handwriting recognition. In this paper, we address the problem of personalization in the context of gesture recognition, and propose a novel and extremely efficient way of doing personalization. Unlike conventional personalization methods which learn a single classifier that later gets adapted, our approach learns a set (portfolio) of classifiers during training, one of which is selected for each test subject based on the personalization data. We formulate classifier personalization as a selection problem and propose several algorithms to compute the set of candidate classifiers. Our experiments show that such an approach is much more efficient than adapting the classifier parameters but can still achieve comparable or better results.

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“…(3,4) at personalization time. We learn a set or portfolio of classifiers at training time and choose the one that performs best on the personalization samples.…”

Section: Learning a Portfolio Of Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gesture Recognition Portfolios for Personalization

Yao

Gool

Kohli

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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“…One such application is to process and retrieve the identities of students for subsequent verification purposes. Thirdly, we can also perform writer-adaptation to create, store or retrieve a profile of handwriting styles of the writers if we are able to automatically determine their identities [5,6]. This way, the performance of the handwriting recognition system can be vastly improved since we are able to customize the recognition system to tailor to the writing profile and style of the writer.…”

Section: Introductionmentioning

confidence: 99%

Automatic writer identification framework for online handwritten documents using character prototypes

Tan

Viard-Gaudin

Kot

2009

Pattern Recognition

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This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.

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“…This technique is known as transfer learning [8], and it has been widely used in applications like handwriting recognition [2], [9], face pose classification [10] etc. Transfer learning may involve (i) Feature transformations, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…If the details of the fonts in the database are known, one could render the textual queries in each of these fonts and retrieve from the database [1]. In some cases, a style clustering [2], [3] is done and then separate classifiers are learnt for each of the style clusters. In this work, we are interested in an effective retrieval solution, where the query is a word image, and the database has an unknown set of fonts.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Word Image Retrieval in Presence of Font Variations

Ranjan

Harit

Jawahar

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-This paper investigates the problem of cross document image retrieval, i.e. use of query images from one style (say font) to perform retrieval from a collection which is in a different style (say a different set of books). We present two approaches to tackle this problem. We propose an effective style independent retrieval scheme using a nonlinear style-content separation model. We also propose a semi-supervised style transfer strategy to expand the query into multiple styles. We validate both these approaches on a collection of word images which vary in fonts/styles.

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Writer adaptation for online handwriting recognition

Cited by 78 publications

References 23 publications

Gesture Recognition Portfolios for Personalization

Gesture Recognition Portfolios for Personalization

Automatic writer identification framework for online handwritten documents using character prototypes

Enhancing Word Image Retrieval in Presence of Font Variations

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