# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 1 Introduction  
Hand gesture recognition is a key part of human communication. Such gestures were the primary means of communication in the prehistoric age [1]. In modern days, hand gestures are still useful, for example, in the case of difficulties with oral communication or human-computer interactions (HCIs). Today, visual experience plays a significant part in HCIs. An interactive and properly gesture-classifiable computer program can properly recognize human behavior as an input for processing. The use of sign language as a gesture-based, as opposed to voice-based, means of communication with another medium makes HCI tremendously promising. The most natural form of gesture-based communication is sign language. Hearing-impaired individuals and people with speech impediments use sign language in their everyday lives. American Sign Language (ASL), which is the most popular form of sign language in the world, is derived from the old French hand symbols and used throughout the continent of North America. Sign language, first developed two centuries ago, is now widely recognized throughout the world. Visual perception plays a vital role in HCI [2]. HCI bridges the gap between machines and humans, allowing them to communicate with each other. Hence, naturally, hand gesturerecognition has been the subject of a significant amount of research in both the communication and machine learning domains. The main idea in the machine learning domain for HCI is to properly classify and detect human gestures and improve the accuracy of the classifier. The main drawback to implementing any kind of machine learning or computer vision-based gesture classification involves the detection and classification of different hand gestures. Theoretically, there are a limited number of hand gestures, and mapping those to the almost infinite number of possible expressions is close to impossible. For this reason, most computer vision-based research solely focuses on classification of a limited number of gestures. On the other hand, HCI studies concern the development of deep learning-based classification and hand gesture detection systems. Deep convolutional neural networks (DCNNs) have recently gained popularity because of their superior ability to recognize visual data. Utilizing DCNNs for object classification is not a recent advancement. Much research has been done, solely focusing on the DCNN-based classification of hand gestures. DCNNs are widely used machine learning algorithms that automate feature extraction and classification without the need for external selective feature extraction methods. This is done by the two (\(2\)) main steps executed by a DCNN algorithm. The convolutional layers in a DCNN utilize convolutional operations in order to extract the important features of input images. Subsequently the fully connected or dense layer of the DCNN classifies the features and produces a prediction of the given input images. The use of a DCNN eliminates the need for human or machine-based feature selection criteria and improves the overall accuracy of classification performance. However, DCNNs have certain fundamental limitations for hand gesture classification. By definition, the input image that is presented to the convolutional layers of a DCNN must have a fixed number of dimensions. This creates problems that are related to resizing or cropping of the input images and results in reduced accuracy and mislabelling due to data loss. Moreover, most research on DCNNs has solely focused on achieving accuracy. Thus, DCNN layers are increasing in size, number, and complexity over time. This means that practical applications of DCNNs are extremely power and computational resource-intensive (in terms of CPU, GPU, RAM, etc.). Addressing high computational costs and optimizing neural networks is a well-researched topic. Many DCNN optimization techniques have been presented in recent years. DCNN node pruning is one such optimization technique, first suggested by [PERSON] et al. [3].