Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata

Akbari, Younes; Nouri, Kazem; Sadri, Javad; Djeddi, Chawki; Siddiqi, Imran

doi:10.1016/j.imavis.2016.11.017

Cited by 55 publications

(40 citation statements)

References 30 publications

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“…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%

“…This study reported an average accuracy of 67.2% using ICDAR 2013 and RDF datasets. In 2017, Akbari et al [35] proposed an effective technique to predict gender that converts a handwritten image into a textured one that is decomposed into various subbands at various levels. These subbands are used to construct Probabilistic Finite State Automata (PFSA) that generate the feature vectors.…”

Section: Related Workmentioning

confidence: 99%

“…Differences in experimental aspects are as follows: the different number and distribution of original images between the categories for training, validating, and testing the classification systems; the different alphabets used; the usage of the same texts written by all the writers or of different texts for each writer; and/or whether there is any preprocessing on the original datasets images. With respect to published results, there are several works [9,35] that only report an overall accuracy result for each classification method used in the problems. In the common case of unbalanced classes, as is the case of "left-handed" in the handedness problem, this overall accuracy is not appropriate and specific measures per each class are more convenient.…”

Section: Comparison With Relatedmentioning

confidence: 99%

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Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

et al. 2018

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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.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With Relatedmentioning

confidence: 99%

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Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

et al. 2018

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“…Akbari et al [2] extracted a feature vector based on a series of wavelet subbands quantized to produce a probabilistic finite state automaton. This feature vector is then used to train ANN and SVM classifiers on the QUWI and MSHD datasets, and perform text-dependent and text-independent, as well as scriptdependent and script-independent classifications (i.e., same/different languages, respectively, used for training and testing).…”

Section: Related Workmentioning

confidence: 99%

Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Illouz

David

Netanyahu

2018

Lecture Notes in Computer Science

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Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writers gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans.

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“…The operation mode of PLS constantly adapts to the changes in the external competitive environment. Therefore, more and more scholars are turning their attention to PLS optimization, especially the rational layout and implementation path [12][13][14]. Duan et al [15] constructed a multi-objective optimization model for the implementation of PLS under complex conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Production Logistics Management of Industrial Enterprises Based on Wavelet Neural Network

Qian¹

2020

JESA

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With an efficient production logistics system, intelligent manufacturers can reduce the investment in production, improve the stability and self-repair ability of production logistics, and strike a perfect balance between production scheduling and production logistics. This paper probes deep into the production logistics management (PLM) of industrial enterprises, and proposes a PLM model for such enterprises based on wavelet neural network (WNN). Firstly, the PLM system architecture of industrial enterprises was established, and the scheduling and task allocation principles were proposed for the collaboration of various subjects in the system. Based on curved time window, a multi-objective path planning and optimization model was established, under influencing factors like the dynamics of station demand and the maximum driving range of handling equipment. Simulation results show that the proposed model is effective in optimizing the path for industrial production logistics. The research results provide theoretical supports to the real-time optimization of PLM and rationalization of production scheduling in industrial enterprises.

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Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata

Cited by 55 publications

References 30 publications

Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks

Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Production Logistics Management of Industrial Enterprises Based on Wavelet Neural Network

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