ICDAR 2013 Competition on Gender Prediction from Handwriting

Hassaine, Abdelaali; Al-Maadeed, Somaya; Alja’am, Jihad Mohamad; Jaoua, Ali

doi:10.1109/icdar.2013.286

Cited by 29 publications

(23 citation statements)

References 12 publications

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“…face or voice), the matter of attempting to devise systems that automatically perform gender classification from handwriting has received but scant attention: the number of available references is really scarce, involving very few researchers (see table 1 for a succinct summary). Nevertheless, this situation may be changing, and the near future may witness an increased interest in this field as the success of the gender prediction contest reported in [32], which attracted 194 participants, suggests.…”

Section: Related Work: Automatic Gender Recognition From Handwritingmentioning

confidence: 99%

Gender Classification by Means of Online Uppercase Handwriting: A Text-Dependent Allographic Approach

2015

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This paper presents a gender-classification schema based on online handwriting. Using samples acquired with a digital tablet that captures the dynamics of the writing, it classifies the writer as a male or a female. The method proposed is allographic, regarding strokes as the structural units of handwriting. Strokes performed while the writing device is not exerting any pressure on the writing surface, pen-up (in-air) strokes, are also taken into account. The method is also text-dependent meaning that training and testing is done with exactly the same text. Text-dependency allows classification be performed with very small amounts of text. Experimentation, performed with samples from the BiosecurID database, yields results that fall in the range of the classification averages expected from human judges. With only four repetitions of a single uppercase word, the average rate of well-classified writers is 68 %; with sixteen words, the rate rises to an average of 72.6 %. Statistical analysis reveals that the aforementioned rates are highly significant. In order to explore the classification potential of the pen-up strokes, these are also considered. Although in this case, results are not conclusive, and an outstanding average of 74 % of well-classified writers is obtained when information from pen-up strokes is combined with information from pen-down ones.

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Section: Related Work: Automatic Gender Recognition From Handwritingmentioning

confidence: 99%

Gender Classification by Means of Online Uppercase Handwriting: A Text-Dependent Allographic Approach

2015

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“…This allows the database to be used in text-independent as well as text-dependent evaluation scenarios. The database was mainly developed to support evaluation of writer identification [149] and writer demographic classification systems [150][151][152] but can also be used for handwriting recognition and similar related tasks.…”

Section: Quwi Databasementioning

confidence: 99%

“…Other than the traditional recognition tasks, competitions on prediction of gender from handwriting have recently gained significant interest. Although a very large number of groups participated in these competitions, a relatively lesser number of groups actually revealed their identities and provided a description of their algorithms [149,150]. In addition to the contests mentioned in Table 3, a number of other competitions on handwritten databases have also been organized but since they employ non-published or private databases, they are beyond the scope of our discussion.…”

Section: Organization Of Competitionsmentioning

confidence: 99%

“…Writer identification rates on the recently developed KHATT database are relatively lower (73.4 %) mainly due to a large number of writers (1000) in the database. The QUWI database which includes writer demographics information has been employed for gender classification in a number of recent studies and a highest classification rate of 69.25 % is realized [150]. Although a two-class problem, gender prediction from handwriting is a challenging task as the correlation between handwriting and gender is not known to be very strong, a major reason for low classification rates.…”

Section: Experimental Protocols Evaluation Metrics and State-of-the-mentioning

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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“…Handwriting recognition has been one of the fascinating and challenging research areas in the field of image processing and pattern recognition [1,2]. It contributes immensely to the advancement of an automation process and can improve the interface between human beings and machines in numerous applications.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for handedness detection from off-line handwriting using fuzzy conceptual reduction

Al-Maadeed

Ferjani

Elloumi

et al. 2016

J Image Video Proc.

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A challenging area of pattern recognition is the recognition of handwritten texts in different languages and the reduction of a volume of data to the greatest extent while preserving associations (or dependencies) between objects of the original data. Until now, only a few studies have been carried out in the area of dimensionality reduction for handedness detection from off-line handwriting textual data. Nevertheless, further investigating new techniques to reduce the large amount of processed data in this field is worthwhile. In this paper, we demonstrate that it is important to select only the most characterizing features from handwritings and reject all those that do not contribute effectively to the process of handwriting recognition. To achieve this goal, the proposed approach is based mainly on fuzzy conceptual reduction by applying the Lukasiewicz implication. Handwritten texts in both Arabic and English languages are considered in this study. To evaluate the effectiveness of our proposal approach, classification is carried out using a K-Nearest-Neighbors (K-NN) classifier using a database of 121 writers. We consider left/right handedness as parameters for the evaluation where we determine the recall/precision and F-measure of each writer. Then, we apply dimensionality reduction based on fuzzy conceptual reduction by using the Lukasiewicz implication. Our novel feature reduction method achieves a maximum reduction rate of 83.43 %, thus making the testing phase much faster. The proposed fuzzy conceptual reduction algorithm is able to reduce the feature vector dimension by 31.3 % compared to the original "BEST OF ALL COMBINED FEATURES" algorithm.

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ICDAR 2013 Competition on Gender Prediction from Handwriting

Cited by 29 publications

References 12 publications

Gender Classification by Means of Online Uppercase Handwriting: A Text-Dependent Allographic Approach

Gender Classification by Means of Online Uppercase Handwriting: A Text-Dependent Allographic Approach

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

A novel approach for handedness detection from off-line handwriting using fuzzy conceptual reduction

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