## 1 Introduction  
According to the World Health Organization (WHO) report, 5% of the world population in 2018 [1], 466 million people, have disabling hearing loss (adults and children comprise 432 and 34 million, respectively), and this is on the rise. Sign language serves as a useful communication medium for communicating with this community and the rest of the community. However, the deaf community, hard of hearing, and those with deaf family members use different body parts for communication in parallel, such as head, arm, or body, finger and hand movement, and facial expression, to provide information. Therein, the hand gesture is a promising part in human-computer interaction and is used for the very practical application of sign language recognition. Sign language recognition is a challenging problem due to the various illumination, uncontrolled environments, complexities of different signs, finger occlusions, and the visual analysis of the gestures of the hand. Therefore, this study focuses on the recognition of the sign word, which is a form of communication tool using hands that helps to reduce the communication gap by introducing a machine interface between the deaf community and the rest of the community. In previous studies, many scholars used hand gestures as a communication medium between human and machine. In [2], the authors described a review of sign language recognition (SLR) based on sensor gloves, which used handshape and movement information. However, the gesture recognition system using hand gloves requires many wearable connectionsfor capturing of hands and finger movements, which prevent the convenience and naturalism of human-computer interaction. This is very difficult and uncomfortable for everyday use. On the other hand, non-wearable devices, such as Microsoft Kinect, RGBD depth camera, leap motion, webcam, and others, have recently become widespread because they does not require attaching sensors to people. However, SLR becomes difficult due to the variety of signs, self-occlusion, background noise, or variation of illumination. In this study, we propose a hybrid segmentation along with a feature fusion of the CNN method to solve the above problems. According to do this, the input of isolated signs word is collected from live video using a webcam, and the proposed hybrid segmentation (YCbCr and SkinMask segmentation) technique is applied for preprocessing the input of hand gestures. The segmented image is then used to extract the features using CNN, and the fusion features are provided for gesture recognition. To achieve this goal, this paper has major contributions as follows. * Hand gesture recognition performance is sub-optimal due to the uncontrolled environment, perspective light diversity, and partial occlusion. Considering the challenges of recognizing the gesture of a sign word, this system proposes a hybrid segmentation strategy that can easily detect the gesture of the hand. Hybrid segmentation can be defined as the coordination of the techniques of two segmentations like YCbCr and SkinMask. YCbCr segmentation converts the input images into YCbCr, then performs binarization, erosion, and fills in the holes. SkinMask segmentation converts the input images into HSV, and the range of H, S, and V values is measured based on the color range of skin. Therefore, the segmented images are provided for feature extraction. * We propose a two-channel strategy of the convolutional neural network, which would be an input YCbCr, and the other would be SkinMask segmented images. The features of segmented images are extracted using CNN, and then, a fusion is applied in the fully connected layer. Furthermore, the fusion feature is fed into the classification process. * A multiclass SVM classifier is used to classify the hand gestures, and the system displays related text. We structured this paper as follows. Section 2 explains the relevant research. In Section 3, we discuss the workflow and propose a model. Datasets and experimental results are described in Section 4.