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Sign language is the most commonly used form of communication for persons with disabilities who have hearing or speech difficulties. However, persons without hearing impairment cannot understand these signs in many cases. As a consequence, persons with disabilities experience difficulties while expressing their emotions or needs. Thus, a sign character detection and text generation system is necessary to mitigate this issue. In this paper, we propose an end-to-end system that can detect Bengali sign characters from input images or video frames and generate meaningful sentences. The proposed system consists of two phases. In the first phase, a quantization technique for the YoloV4-Tiny detection model is proposed for detecting 49 different sign characters, including 36 Bengali alphabet characters, 10 numeric characters, and 3 special characters. Here, the detection model localizes hand signs and predicts the corresponding character. The second phase generates text from the predicted characters by a detection model. The Long Short-Term Memory (LSTM) model is utilized to generate meaningful text from the character signs detected in the previous phase. To train the proposed system, the BdSL 49 dataset is used, which has approximately 14,745 images of 49 different classes. The proposed quantized YoloV4-Tiny model achieves a mAP of 99.7%, and the proposed language model achieves an overall accuracy of 99.12%. In addition, performance analysis among YoloV4, YoloV4 Tiny, and YoloV7 models is provided in this research.