CASIA-OLHWDB1: A Database of Online Handwritten Chinese Characters

Wang, Da‐Han; Liu, Cheng‐Lin; Yu, Jinlun; Zhou, Xiangdong

doi:10.1109/icdar.2009.163

Cited by 32 publications

(21 citation statements)

References 13 publications

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“…In order to assess character segmentation algorithms, a database of touching Chinese characters was compiled from the CASIA handwriting database [187,188]. This database was termed as CASIA-HWDB-T [194].…”

Section: Touching Character Databasementioning

confidence: 99%

“…CASIA [187,188] is a widely used Chinese handwritten database comprising handwritten paragraphs as well as isolated characters The data was collected from 1020 individuals who produced writings on paper with a digital pen. This allowed capturing the online trajectory information as well as the offline images of text.…”

Section: Casia: Online and Offline Chinese Handwriting Databasesmentioning

confidence: 99%

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A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

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Handwriting has remained one of the most frequently occurring patterns that we come across in everyday life. Handwriting offers a number of interesting pattern classification problems including handwriting recognition, writer identification, signature verification, writer demographics classification and script recognition, etc. Research in these and similar related problems requires the availability of handwritten samples for validation of the developed techniques and algorithms. Like any other scientific domain, the handwriting recognition community has developed a large number of standard databases allowing development, evaluation and comparison of different techniques developed for a variety of recognition tasks. This paper is intended to provide a comprehensive survey of the handwriting databases developed during the last two decades. In addition to the statistics of the discussed databases, we also present a comparison of these databases on a number of dimensions. The ground truth information of the databases along with the supported tasks is also discussed. It is expected that this paper would not only allow researchers in handwriting recognition to objectively compare different databases but will also provide them the opportunity to select the most appropriate database(s) for evaluation of their developed systems.

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Section: Touching Character Databasementioning

confidence: 99%

Section: Casia: Online and Offline Chinese Handwriting Databasesmentioning

confidence: 99%

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

Hussain

Raza²,

Siddiqi

et al. 2015

J Image Video Proc.

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“…For online version of OR3C dataset, we use a stateof-the-art recognizer [9] [10], the experimental setting is similar with [6]. First, we reduce the dimension of images to 64×64, normalize all the images with pseudo 2D moment normalization method.…”

Section: A Experiments With Online Datasetmentioning

confidence: 99%

“…The unconstrained character recognition remains one of the most challenging tasks [5]. One of the most critical bottlenecks for improving its recognition performance is the short of available large-scale unconstrained handwriting dataset [6].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Evaluation on HIT-OR3C Database

Zhou

Chen

Wang

et al. 2011

2011 International Conference on Document Analysis and Recognition

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Recently, we have proposed a handwriting Chinese character database HIT-OR3C. Though it has been introduced in detail, to date, it has not been evaluated by any handwriting recognition method. To help the researchers use this database for algorithm evaluation, we propose the structure of HIT-OR3C database. Moreover, we evaluate the OR3C database with a series of experiments using state-of-the-art handwriting recognizer. These experiment results on the different subsets can be a benchmark for the researchers who will use the database. The low average recognition rate confirms that the HIT-OR3C database is challenging.

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“…Motivated by these, many researchers devoted themselves to the field of handwriting character recognition and achieved great progress during the past 40 years [1][2][3]. However, recent researches [4][5][6] have shown that the recognition accuracy using state-of-the-art techniques cannot satisfy user's expectations [7].…”

Section: Introductionmentioning

confidence: 99%

Incremental MQDF Learning for Writer Adaptive Handwriting Recognition

Ding

Jin

2010

2010 12th International Conference on Frontiers in Handwriting Recognition

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Writer adaptation has been proved to be an effective approach to improve the recognition performance of the writer-independent recognizer for a particular writer. In this paper, we propose a writer adaptive handwriting recognition approach by incremental learning the Modified Quadratic Discriminant Function (MQDF) classifier. We derived the solution of Incremental MQDF (IMQDF) and then present a Discriminative IMQDF (DIMQDF) by deriving the solution of IMQDF in the updated discriminative feature space. Based on IMQDF or DIMQDF, the writer adaptation is finally performed by updating the MQDF recognizer adaptively. The experimental results for recognizing handwriting Chinese characters indicate that the proposed IMQDF and DIQMDF approaches can reduce as much as 52.71% and 45.38% error rate respectively on the writer-dependent dataset while only have less than 0.18% accuracy loss on the writer-independent dataset. In other words, the proposed IMQDF and DIMQDF based writer adaptation approaches can significantly increase the recognition accuracy on writer-dependent dataset while only have limited negative influence for general writer.

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CASIA-OLHWDB1: A Database of Online Handwritten Chinese Characters

Cited by 32 publications

References 13 publications

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation

An Empirical Evaluation on HIT-OR3C Database

Incremental MQDF Learning for Writer Adaptive Handwriting Recognition

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