# Visual Static Hand Gesture Recognition Using Convolutional Neural Network
## 1 Introduction
In the proposed method, we will solve that by working on datasets with complex backgrounds. [PERSON] et al. [11] derived a \(CNN\) architecture from a directed acyclic graph (\(DAG\)) structure (\(DAG\)-\(CNN\)). A self-constructed dataset was used to experiment with the model on. The dataset consists of 10 gestures for controlling the robotic arm, and model accuracy was 84.5%. However, the model's performance may vary when applied to different gesture recognition tasks. Using fully connected layers of a pre-trained artificial neural network (AlexNet), [PERSON] et al. [12] proposed a deep \(CNN\) feature-based static hand gesture recognition system. This system reduces redundant features using principal component analysis after deep features are extracted using fully connected layers of AlexNet (\(PCA\)). An \(SVM\) was then used as a classifier to categorize the poses of hand motions. The dataset was developed from five subjects with 36 gesture poses (10 ASL digits and 26 ASL alphabets). The dataset has variations in illumination in five different directions, such as left, right, top, bottom, and diffuse. The gesture poses are performed with variation in hand rotation, scale, and articulation. The American Sign Language dataset was used to test the system's performance on 36 gesture postures, and the average accuracy score was 87.83%. [PERSON] et al. [13] proposed a generic \(CNN\) architecture for static sign language recognition. The dataset used comprises 35,000 images, which include 350 images for each of the static signs. There are 100 distinct sign classes that include 23 alphabets of English, 0-10 digits, and 67 commonly used words (e.g., bowl, water, stand, hand, fever, etc.). The network had an accuracy of 98.85% on the Indian sign language dataset. However, this approach may face challenges when applied to complex backgrounds, as the dataset consists of images captured on a simple white background that can be identified as relatively unchallenging. For the purpose of assessing human behavior in the context of classroom learning and instruction with two teachers, [PERSON] et al. [14] introduced a recognition model of hand gestures based on _CNN_. The analysis of the teacher's nonverbal behaviors that improve the learning results of learners and attract their attention is achieved by exploiting the recognized instructors' hand gestures. A non-linear neural network with four convolutional layers is used in this model to extract the features of hand gesture images. For achieving robust recognition, three convolution layers of _CNN_ are designed. A dataset of 38,425 infrared hand gesture images extracted from 100 short infrared videos is used to test and evaluate the model. The model has reached an accuracy of more than 92%. The model has the advantage of using a dataset that gives good results. Nevertheless, the images are also simple, with simple infrared backgrounds. In ref. [15], [PERSON] et al. suggested a method for identifying hand gestures through the use of particle filtering. By implementing this filtering technique on hand gesture images with identical backgrounds, the researchers achieved an accuracy of 90.6%. However, it has the disadvantage that performance may degrade when backgrounds are not stationary. In ref. [16], [PERSON] and [PERSON] utilized shape features obtained from hand gesture images to categorize them into different classes. The extracted features were then taught and sorted into clusters using the k-means clustering algorithm. This approach offers simplicity but may struggle with complex hand gestures. In ref. [17], [PERSON] et al. put forward a method for hand gesture recognition that involved the use of a convexity algorithm approach. The researchers tested this technique on hand gesture images with identical backgrounds and obtained an accuracy of 87.5%. However, the approach was limited by the fact that the algorithm worked best when the background of the hand gesture image was stationary. In ref.