An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 & VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collected from different volunteers. The pre-trained models were used and trained on prepared Arabic sign language data. These models were used after some modification. Also, an attempt has been made to adopt two models from the previously trained models, where they are trained in parallel deep feature extractions. Then they are combined and prepared for the classification stage. The results demonstrate the comparison between the performance of the single model and multi-model. It appears that most of the multi-model is better in feature extraction and classification than the single models. And also show that when depending on the total number of incorrect recognize sign image in training, validation and testing dataset, the best convolutional neural networks (CNN) model in feature extraction and classification Arabic sign language is the DenseNet121 for a single model using and DenseNet121 & VGG16 for multi-model using.

<p>In computer vision, one of the most difficult problems is human gestures in videos recognition Because of certain irrelevant environmental variables. This issue has been solved by using single deep networks to learn spatiotemporal characteristics from video data, and this approach is still insufficient to handle both problems at the same time. As a result, the researchers fused various models to allow for the effective collection of important shape information as well as precise spatiotemporal variation of gestures. In this study, we collected the dynamic dataset for twenty meaningful words of Arabic sign language (ArSL) using a Microsoft Kinect v2 camera. The recorded data included 7350 red, green, and blue (RGB) videos and 7350 depth videos. We proposed four deep neural networks models using 2D and 3D convolutional neural network (CNN) to cover all feature extraction methods and then passing these features to the recurrent neural network (RNN) for sequence classification. Long short-term memory (LSTM) and gated recurrent unit (GRU) are two types of using RNN. Also, the research included evaluation fusion techniques for several types of multiple models. The experiment results show the best multi-model for the dynamic dataset of the ArSL recognition achieved 100% accuracy.</p>

Traffic signs in general and speed limit signs in particular are considered one of the most important means of traffic safety, and the aim of the current research is to design a system that detects and recognizes speed limit sign with high accuracy and high processing speed. At the beginning, red color objects are detected from the image and after finding the red color signs the circle is determined using Hough's transform then from inside the circle, the numeric part from the circle image is extracted. Digital circle images are segmented to extract the number alone, and then these numbers are recognized by a trained neural network. Neural network achieved a success rate in recognition reached to 98.9%. Parallel programming concept is used to reduce the execution time using OpenMP and OpenCl programming. The study showed that the total execution speed according to the designed scheme to run the speed limit sign detection and recognition by using a mix of central processing unit with multi cores and graphics processing unit is 65 frames/sec for complete images and 90 frames/sec when cropping the effective part from the total size of the image. Recognition system is capable of recognizing the sign even if the vehicle speed exceeds 120 km/h.