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An example of two layers feed forward network architecture.
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This research present's a new approach for Arabic Text-dependent Writer Identification. It dependent on feature based classification approach using the Discrete Contourlet transform. The coefficient of CT is used as a feature vector. This feature vector is used to extract the Eigen value and Eigen vector using PCA method. The PCA (Principal Compone...
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Citations
... In [19], Djeddi Chawki and colleagues proposed a global texture analysis method, treating each writer's handwriting as distinct textures, successfully identifying and verifying 130 handwritten images from 650 different Arabic writers. In [20], Fathi H utilized a discrete contour transformation approach with MLP neural network classification to distinguish 50 Arabic writers. In [21], Tayeb Bahram treated contours as textures, computing the joint probability distribution of binary patterns (MLBP) and ink width and shape letter (IWSL) at different pixel locations, achieving excellent results across eight prominent handwriting datasets. ...
Handwriting is a biometric behavioral characteristic with evident individual distinctiveness. With the rise of the deep learning trend and demands for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. Research in handwriting identification for major global languages has matured. However, in China, there is limited attention in the field of writer identification for minority languages such as Mongolian, making it challenging to resolve criminal cases involving handwriting issues. This paper initiates an initial exploration of Mongolian handwriting identification by constructing a structurally simple convolutional neural network. This convolutional neural network, consisting of 12 convolution operations and designed for Mongolian handwriting identification, is referred to as MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 writers from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet50, Fragnet, GRRNN, VGG16, and VGG19 models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification. The exploratory research on Mongolian handwriting identification in this paper contributes to increasing awareness of information processing for minority languages. It aids in advancing research on classifying writers of Mongolian historical texts and provides technical support for judicial authentication involving handwriting issues.
... In [17], Djeddi Chawki and team proposed a global texture analysis method, treating each writer's handwriting as distinct textures, successfully identifying and verifying 130 handwritten images from 650 different Arabic authors. In [18], Fathi H utilized a discrete contour transformation approach with MLP neural network classification to distinguish 50 Arabic authors. In [19], Tayeb Bahram treated contours as textures, computing the joint probability distribution of binary patterns (MLBP) and ink width and shape letter (IWSL) at different pixel locations, achieving excellent results across eight prominent handwriting datasets. ...
Handwriting is a form of biometric behavioral characteristic with evident individual distinctiveness. With the surge of the deep learning trend and the demand for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. The research on handwriting identification in major world languages has reached a mature stage. However, there is still a notable lack of relevant research in the field of Mongolian handwriting identification, despite the fact that Mongolian is used by over 4 million people in China. This paper embarks on an initial exploration of Mongolian handwriting identification by constructing a convolutional neural network named MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 authors from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet, Fragnet, and GRRNN models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification.
