Off-Line Signature Recognition Based on Angle Features and Artificial Neural Network Algorithm

Handwritten signature recognition is presented based on an angle feature vector by using the artificial neural network （ANN） in this research. Each signature image will be represented by an angle vector. The feature vector will constitute the input to the ANN. The collection of signature images is divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested by recognizing the signatures. When a signature is classified correctly, it is considered correct recognition, otherwise it is a failure. The achieved recognition rate of this system is 94%.

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

A New Delaunay Triangulation Algorithm Based on Constrained Maximum Circumscribed Circle

Delaunay triangulation is gradually playing an important role in the field of finite element analysis, image recognition, and medical visualization.Considering the quality and partition efficiency, a new Delaunay triangulation method based on constrained maximum circumscribed circle is proposed. First, according to two important criteria, the empty circle features and the maximized minimum angle characteristics, we established constrained conditions. Then, we iterated the container vertices, structure triangular face linked lists, and finally got the Delaunay data. The experimental results showed that the efficiency of the improved triangulation dissection method increased by 9.47% compared with traditional triangulation algorithms in irregular triangle vertex data.