## 2 Literature Review
However, their proposed model could not work correctly to recognize critical sign gestures. [PERSON] et al. [26] developed a computer system to recognize 36 different classes of BSL and translated them into text format. For sign recognition, they used the MobileNet version 1 model and got an accuracy of 95.71%. [PERSON] et al. [27] designed a sign language transformer that can aid in establishing communication with doctors remotely by translating sign language into text and speech. It also translates the speeches of doctors into sign language. However, the system is not yet tested for people with disabilities in Bangladesh. A Scale-Invariant Feature Transform (SIFT) technique and Convolutional Neural Network (CNN) are used in the proposed system of [7] to recognize one-handed gestures of 38 Bangla Signs. However, the used dataset is relatively low. [PERSON] et al. [9] proposed a translation system that can translate different Bengali word signs into Bengali speech using a seven-layered custom sequential CNNmodel. The system trained with 1500 images of 10 different word signs and achieved 97% test accuracy as well as an average response time of 120.6 ms. The authors in [5] proposed a method to detect and recognize American Sign Language using the YOLOv5 algorithm and achieved an overall F1-score of 0.98. However, their dataset contains only 36 different sign classes. The authors of [13] proposed a real-time Bangla sign-language detection model and generated textual and audio speech. They created a dataset of 49 different classes containing 12,500 images of BDSL. They used Yolov4 for detecting hand gestures and got an overall accuracy of 97.95%. However, while generating some common words, the system faces some difficulties. A rule-based system is proposed by the authors of [28] to interpret Bengali text and voice speech into BSL. The system achieved an accuracy of 96.03% for voice interpretation and 100% for text translation. However, the system was trained and tested with only Bangla numerals. [PERSON] et al. [29] proposed a CNN and customized Region-of-Interest (ROI) segmentation-based BDSL translator device that can translate only five sign gestures. It demonstrates about 94% accuracy in detecting signs in real-time. [PERSON] et al. [30] proposed a hybrid model for automatic recognition of Bangla Sign Language (BSL) numerals and alphabet characters using a deep transfer learning-based convolutional neural network with a random forest classifier. The proposed system is verified on 'IsharaBochon' and 'Ishara-Lipi' datasets and achieves 91.67% and 97.33% accuracy, respectively, for both character and digit recognition. The authors of [31] proposed a method that includes three steps: segmentation, augmentation, and CNN-based classification, and is evaluated on three benchmark datasets. The segmentation approach accurately identifies gesture signs using a concatenated approach with YCbCr, HSV, and a watershed algorithm. A CNN-based model called BenSignNet is applied to extract features and for classification. [PERSON] et al. [32] present a low-cost Bangla sign language recognition model that uses neural networks to convert signs into Bangla text. The dataset was manually captured, and image processing techniques were used to map actions to relevant text. The system achieves an accuracy of approximately 92% and can be used for applications such as sign language tutorials or dictionaries. [PERSON] et al. [33] proposed a real-time Bangla Sign Language (BdSL) detection system that aims to generate Bangla sentences from a sequence of images or a video feed. The system uses the Blazepose algorithm to detect sign-language body postures and a Long Short-Term Memory (LSTM) network to train the data. After training for 85 epochs, the model achieved a training accuracy of 93.85% and a validation accuracy of 87.14%. The authors of [34] are working on isolated sign language recognition using PyTorch and YOLOv5 for video classification. The system aims to help people with hearing and speech disabilities to interact with society.