###### Abstract  
The deaf and hearing-impaired community expresses their emotions, communicates with society, and enhances the interaction between humans and computers using sign language gestures. This work presents a strategy for efficient feature extraction that uses a combination of two different methods that are the convolutional block attention module (CBAM)-based convolutional neural network (CNN) and standard handcrafted histogram of oriented gradients (HOG) feature descriptor. The proposed framework aims to enhance accuracy by extracting meaningful features and resolving issues like rotation, similar hand orientation, etc. The HOG feature extraction technique provides a compact feature representation that signifies meaningful information about sign gestures. The CBAM attention module is incorporated into the structure of CNN to enhance feature learning using spatial and channel attention mechanisms. Then, the final feature vector is formed by concatenating these features. This feature vector is provided to the classification layers to predict static sign gestures. The proposed approach is validated on two publicly available static Massey American Sign Language (ASL) and Indian Sign Language (ISL) databases. The model's performance is evaluated using precision, recall, F1-score, and accuracy. Our proposed methodology achieved 99.22% and 99.79% accuracy for the ASL and ISL datasets. The acquired results signify the efficiency of the feature fusion and attention mechanism. Our network performed better in accuracy compared to the earlier studies. sign language recognition; feature extraction; deep learning; HOG features; gestures +
Footnote †: journal:  
classical machine learning algorithms that perform feature extraction and classification as two individual steps. However, with the growth of intelligent deep neural networks and the advancement in artificial intelligence, significant improvement has been made in image and video processing. The convolutional layers in the convolutional neural network (CNN) extract many low-level features like edges, corners, curves, etc., and high-level features like object detection from images. There are various classical feature descriptors like scale-invariant feature transform (SIFT), local binary patterns (LBP), and speeded-up robust features (SURF) for the extraction of features [2; 3; 4]. However, most of these features are subject to scaling, rotation, and brightness variations. So, to solve these issues, a system is needed that uses both traditional handcrafted feature learning methods and deep learning-based models. Nowadays, most researchers use attention-based networks to enrich the model's efficiency and minimize the computational cost. The main objective of these networks is to focus on relevant features and remove redundant features to classify the gestures [5] accurately. In our proposed methodology, the sign language gesture images are pre-processed and fed to a feature extraction module that extracts specific features from two strategies: histogram of oriented gradients (HOG) and convolutional block attention module (CBAM) attention-based CNN. Finally, the merged features produce a feature vector. Combining two models aims to implement a highly robust architecture to various uncertainties and achieve reasonable accuracy. The summary of the significant contributions of this research work is as follows:  
1. We implemented an efficient feature extraction technique using the fusion of HOG feature descriptor and attention-based CNN for recognizing hand sign gestures. 2. The CBAM module is embedded in CNN to enhance the learning of features at various scales so that the model can handle different alterations in the location and shape of hand gestures and focus on effective features. 3. We tested the efficiency of the approach among two datasets using three evaluation metrics, namely precision, recall, and F1-score.