Bangla Sign Language is a unique sign language. Due to a lack of interpreters, the hearing- and speech- impaired community face challenges while communicating with the broader community. Recent studies have been con- ducted to reduce the gap between these two communities. But most of the researchers used a dataset with a controlled environment. We know the performance of a system highly depends on dataset quality. In this paper, we have created a new dataset, “BanglaSignSet” including 46 unique signs with over 10k images. We have carefully annotated and labeled the images using Roboflow. Our proposed dataset, “BanglaSignSet” consists of images with high resolution, good qual- ity, and adequate variation in environment and person. The constructed dataset has been trained using the most recent deep learning model, such as YOLOv8. We have also implemented different versions of the YOLOv8 model, such as YOLOv8n, YOLOv8s, and YOLOv8m. Additionally, we evaluated EfficientNet-B0 as a classification-based baseline to broaden the experimental comparison. The performance of models has been measured using different evaluation metrics such as mAP, precision, recall, and f1 score. A comparative analysis has been conducted based on the performance of the model. By comparative analysis we found a well-suited model, YOLOv8n, to deploy into a web-based application. To find the suitable model to deploy, we have considered factors such as memory requirement and inference speed. We have integrated the YOLOv8n model into a web application using the Python language. We have also tested the web application on Android devices and laptops. The web application detects signs from image input successfully.