## 1 Introduction  
The most-widespread communication means for persons with hearing or speech disabilities is sign language. Bengali Sign Language (BSL) is composed of precise hand motions produced with one or two hands, where the arrangement of the fingers and the position of the palm together form the characters of the language [1,2]. The signs that are used to represent Bengali characters and numerals are called BSL. In Bangladesh, there are over 1.7 million people who have hearing impairment or speech impairment. These hearing- or speech-impaired individuals use BSL for communication. However, due to the lack of knowledge of sign language, persons with such disabilities cannot convey their opinions and emotions properly. To address this issue, an interpreter system is needed that can detect Bengali signs characters and translate them into Bengali text. It can reduce the communication gap between person with and without disabilities. Currently, most research emphasizes vision-based [3] or sensor-based [4] systems. However, further research needs to be done in this domain to introduce an efficient and convenient system for sign-language interpretation. At present, various benchmarking datasets for BdSL detection can be found, and there has been much research on the subject. However, these datasets are insufficient for developing and testing deep learning models,and most of them are closed-source. As a result, it becomes difficult to train Convolutional Neural Network (CNN) models to detect BSL. Sign-language detection is one of the challenging tasks in computer vision. Numerous related works [5; 6; 7; 8; 9] on detecting the individual characters of Bangla Sign Language can be found. However, hardly any work has been done with generating text from sign images. Many research studies only included around 25-35 sign characters for their detection models. The dataset we have used, has 49 different characters. Additionally, researchers use various techniques, i.e., K-Nearest Neighbors (KNN), Support Vector Machine (SVM), shallow neural networks, and image processing, for detection purposes. However, these models are trained on a small dataset consisting of only 2000-3000 pictures in total. Furthermore, previous works not only used a smaller amount of data but also a smaller number of classes. The maximum number of classes was 36, which was proposed by [PERSON] et al. [10]. The YoloV4 [11] detection model is quite popular among researchers for detecting sign language [12; 13]. Researchers [13] developed a BSL detection model using Yolov4. In addition to detecting or localizing the region of the waved sign in an image or video frame, the YoloV4 model also predicts the class or label of which sign is being waved. In a nutshell, this detection model also recognizes the localized area of the hand sign. Researchers have utilized the YoloV4 model to detect sign language, which is a popular technique in detection systems. However, this research focuses on optimizing the YoloV4-Tiny model by using quantization techniques. The output labels of the proposed detection model are passed to the LSTM-based text generation model for generating meaningful words from the resultant characters. As far as current literature is concerned, there appears to be a dearth of studies pertaining to the detection of Bangla hand signs through implementation of a YoloV4-Tiny model, and no quantization technique has been put forth in this regard. In this work, we propose an end-to-end system that can detect Bengali sign characters and generate coherent phrases through offering a novel quantization method for the YoloV4-Tiny model (for detecting Bangla hand signs) and LSTM model (for generating meaningful sentences from detected Bangla hand signs). A BdSL 49 dataset consisting of 49 distinct characters, including 36 alphabet characters, 10 numeric characters, and 3 special characters, is used to train the detection model. The dataset has a total of 14,745 images for 49 different characters.