Automatic handedness detection from off-line handwriting

Al-Maadeed, Somaya; Ferjani, Fethi; Elloumi, Samir; Hassaine, Abdelaali; Jaoua, Ali

doi:10.1109/ieeegcc.2013.6705761

Cited by 12 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In all cases, the run-length features outperform these features. Finally, for the comparison of the proposed method with other methods, the average correct handedness detection results are over 83.43 %, which exceeds the results reported in [30] for off-line gender identification (70 %) on the same dataset. The results also compare well with the 73 %; 55.39 % reported for gender classification in [36,43] on different datasets.…”

Section: Discussionmentioning

confidence: 61%

“…Other applications including such as off-line handwriting recognition systems for different languages reached up to 99 % for handwritten characters [29]. For handedness detection our earlier study proved that it can work [30]. Authors in [25] evaluated the performance of edgebased directional probability distributions as features in comparison to a number of non-angular features.…”

Section: A Review Of Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 61%

Section: A Review Of Related Workmentioning

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regarding the complexity of the proposed model with respect to classical approaches (i.e., feature-based ones), from a developer's viewpoint using convolutional neural networks (CNN) is simpler than determining which features are the best ones for discriminating each class. Differently from other analyzed feature-based proposals (see, e.g., [11,35,40]), when using CNN one does not have to discover which features are relevant to solve the problem (i.e., this approach is a drop-in replacement to hand-made feature descriptors). Since these good internal representations are now found by the network, the model is much simpler and powerful at the same time.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…In 2007 Liwicki et al [10] also proposed an online method for handedness detection using SVM and GMM for classification using the IAM database and reported results of 62% with SVM and 84.6% with GMM, respectively. Al-Maadeed and others [40] studied in 2013 the offline handedness classification problem (i.e., without using dynamic information from handwriting). They extracted shape and curvature features from strokes and used a knn classifier, reporting results of 71.5% on the QUWI database (with both English and Arabic texts).…”

Section: Related Workmentioning

confidence: 99%

Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

et al. 2018

View full text Add to dashboard Cite

Demographic handwriting-based classification problems, such as gender and handedness categorizations, present interesting applications in disciplines like Forensic Biometrics. This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively. Our research was carried out on two public handwriting databases: the IAM dataset containing English texts and the KHATT one with Arabic texts. The considered problems present a high intrinsic difficulty when extracting specific relevant features for discriminating the involved subclasses. Our solution is based on convolutional neural networks since these models had proven better capabilities to extract good features when compared to hand-crafted ones. Our work also describes the first approach to the combined gender-and-handedness prediction, which has not been addressed before by other researchers. Moreover, the proposed solutions have been designed using a unique network configuration for the three considered demographic problems, which has the advantage of simplifying the design complexity and debugging of these deep architectures when handling related handwriting problems. Finally, the comparison of achieved results to those presented in related works revealed the best average accuracy in the gender classification problem for the considered datasets.

show abstract

“…Experiments conducted on IAM dataset reported an accuracy about 67.7%. Furthermore, in [5], [6], [7] authors employed a private dataset of Arabic handwritten sentences to predict writers gender, age range and nationality. Prediction schemes including, SVM, neural networks, random forest and linear discriminate analysis provided scores around 70%.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Integral for Combining SVM-Based Handwritten Soft-Biometrics Prediction

Bouadjenek

Nemmour

Chibani

2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

View full text Add to dashboard Cite

This work addresses soft-biometrics prediction from handwriting analysis, which aims to predict the writer's gender, age range and handedness. Three SVM predictors associated each to a specific data feature are developed and subsequently combined to aggregate a robust prediction. For the combination step, Sugeno's Fuzzy Integral is proposed. Experiments are conducted on public Arabic and English handwriting datasets. The performance assessment is carried out comparatively to individual systems as well as to max and average rules, using independent and blended corpuses. The results obtained demonstrated the usefulness of the Fuzzy Integral, which provides a gain of more than 4% over individual systems as well as other combination rules. Moreover, with respect to the state of the art methods, the proposed approach seems to be much more relevant.

show abstract

Automatic handedness detection from off-line handwriting

Cited by 12 publications

References 12 publications

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

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

Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

Fuzzy Integral for Combining SVM-Based Handwritten Soft-Biometrics Prediction

Contact Info

Product

Resources

About