## 1 Introduction  
The deaf and hard of hearing (DHH) people can do many things in their daily lives, excluding communication. Although, communication is inevitable for passing the message and expressing one's ideas, thoughts, and social identity to others. DHH community used sign language to communicate with others instead of the common language. This is also the primary mode of communication for the DHH community in Bangladesh, although it is difficult for general people to communicate with deaf and mute individuals. Sign language is a structural form of hand gestures involving signs of different body parts, such as fingers, hands, arms, head, body, and facial expressions, that are used as a communication system to help the deaf and speech-impaired community for daily interaction [1]. There are over 70 million deaf and hard of hearing people worldwide [2], with nearly 3 million deaf people in Bangladesh [3]. Moreover, due to a lack of social awareness, the deaf and hard of hearing group has communication difficulties on their own. For this, the primary and everyday activities such as education [4; 5], medical services, employment,and socializing have been challenged. In the given scenario, a sign language interpreter is required for communication between the general and deaf communities. However, an experienced translator may not always be accessible, and paying reasonable costs in such cases may be a major concern. As a solution, automatic sign language recognition can play an essential role in bridging the fundamental and social gap between the deaf and healthy communities. Nowadays, researchers are focused on two domains to develop automated sign language systems [6], such as sensor-based [7] and vision-based approaches [8]. Most BSL researchers focus on vision-based approaches with machine learning and the deep learning technique [9; 10], which capture movements statically or dynamically utilizing one-handed and two-handed techniques. There are a few systems has been developed for BSL recognition. Moreover, the benchmark dataset on BSL is inadequate, and there are few benchmark datasets that are freely available to evaluate the existing model and accurately train the deep learning model. Among them, 38 BdSL datasets introduced by [PERSON] et al., which contained 12,160 samples, produced performance accuracy using deep learning is 89.60%, which is very low for implementing a BSL recognition system [11]. KU-BdSL and Ishara-Lipi also considered benchmark datasets of BSL that are publicly available [12; 13]. Most of the BSL researchers are conducting their research based on their own datasets [9; 14; 15]. The authors achieved a satisfactory level of accuracy on the small author-created datasets; however, these systems may fail to produce a good performance for other benchmark datasets. On the other hand, a diverse variety of sign gestures, a complicated environment, partial occultation, redundant background, and viewpoint of light variation are frequent problems for achieving high performance in BSL recognition. By considering these challenges, a generalized system for BSL recognition is inevitable for implementing a real-time BSL recognition system. This study aims to investigate the current state of the art system for BSL recognition and deploy a generalized BSL recognition system to increase the performance for the real-world scenarios where the data come from different diverse. The main contribution of this paper is described below:  
* We proposed concatenated segmentation techniques to solve light illumination, uncontrolled environment and background noise. Segmentation techniques consist of YCbCr, HSV, morphology and watershed algorithms. * We used seven augmentation approaches to generate diverse sign images such as rotated, translated, scaled, flipped from the input image in order to enlarge the dataset, deal with inefficient deep learning model training and keep the model image diversity invariant.