## 2 Literature Review  
Sign Language Recognition (SLR) is a significant computer vision task that has been extensively studied. The prevalent SLR approaches can be categorized into two groups: recognizing a single sign at a time [14] and recognizing multiple signs from video content input [15]. However, there is lack of sufficient study for Bangla SLR and/or detection. This section discusses previous work related to Bangla SLR and detection. [PERSON] et al. [16] proposed a dataset of commonly used Indian Sign Language (ISL) words for hearing-impaired COVID patients in emergency cases. They also presented a deep convolutional LSTM hybrid model for recognizing the proposed ISL words and acquired an accuracy of 83.36%. The authors in [17] developed a system that can recognize Arabic hand signs of 31 classes and translates them into Arabic speech. The system provides 90% for recognizing Arabic hand signs. The authors propose a deep multi-layered CNN model in [8] to detect 36 static English signs, including digits and alphabet characters and 26 dynamic English signs such as emotional hand gestures. Their proposed model achieved 99.89% accuracy in the blind testing phase. One drawback of this model is that it cannot detect signs in real time. [PERSON] et al. [18] proposed a grammar-based translation model to translate English sentences into equivalent Pakistani Sign Language and got a Bilingual Evaluation Understudy (BLEU) score of 0.78. However, the model is unable to translate compound and complex sentences correctly. For Bangla, OkkhorNama is an image dataset is proposed in [19] for real-time object detection. It contains over 12,000 images of 46 classes. The dataset is used to train different versions of YOLOv5 for performance analysis. However, the number of classes is not significant. Afterwards, a dataset named BdSL36, which includes 26,713 images of 36 different classes, was proposed by [10]. This dataset is further divided into two sections. The first section contains images for developing a detection model, and the second section contains images for developing a recognition model. To evaluate their dataset, the authors trained ResNet-50 on their dataset and achieved an accuracy of 98.6%. Although the number of images in the dataset is significantly large due to data augmentation, background removal, affine transformation, and many other methods applied, it only has 36 classes. The authors of [20] developed a dataset named Shongket, which consists of 36 classes of distinct Bengali signs. They also trained a few CNN models using their dataset and achieved about 95% accuracy. The dataset BDSLInfinite, consisting of 37 different signs, was developed by [PERSON] et al. [6] and contains 2000 images, and a trained recognition model using Xception architecture achieved 98.93% accuracy as well as 48.53 ms response time. The authors of [21] proposed a custom CNN architecture and trained it on a dataset that consists of 100 different static sign classes. On colored and grayscale images, the proposed method obtained training accuracy rates of 99.72% and 99.90%, respectively. [PERSON] et al. [22] proposed an integrated CNN-LSTM model for Bangla Lexical Sign Language Recognition using a dataset that contains 13,400 images of 36 distinct classes of Bangla lexical signs. Their proposed model achieved 90% training accuracy and 88.5% testing accuracy. Moreover, they used high computational techniques for data pre-processing, which made their model computationally expensive. An application that speaks the results in Bangla after automatically detecting hand-sign-based digits was developed by the authors of [23]. They utilized deep neural networks to complete the task, and their proposed model achieved an accuracy of 92%. [PERSON] et al. [24] used four different finely tuned transfer learning methods to recognize 11 different Bengali sign words and got an accuracy of 98%. [PERSON] et al. [25] proposed a Bangla sign language recognition and sentence generation model and achieved 90% accuracy using their augmented dataset.