Real Time Bangladeshi Sign Language Detection using Faster R-CNN

Bangladeshi Sign Language (BdSL) is a commonly used medium of communication for the hearing-impaired people in Bangladesh. Developing a real time system to detect these signs from images is a great challenge. In this paper, we present a technique to detect BdSL from images that performs in real time. Our method uses Convolutional Neural Network based object detection technique to detect the presence of signs in the image region and to recognize its class. For this purpose, we adopted Faster Region-based Convolutional Network approach and developed a dataset $-$ BdSLImset $-$ to train our system. Previous research works in detecting BdSL generally depend on external devices while most of the other vision-based techniques do not perform efficiently in real time. Our approach, however, is free from such limitations and the experimental results demonstrate that the proposed method successfully identifies and recognizes Bangladeshi signs in real time.Comment: 6 pages, Accepted in International Conference on Innovation in Engineering and Technology (ICIET) 27-29 December, 2018, Dhaka, Banglades

COVID-19 Non-Pharmaceutical Interventions: Data Annotation for Rapidly Changing Local Policy Information

Main Dataset Attributes (npi_data/*.xlsx) FIPS: FIPS of the county. Location name: Name of the county. NPI measure: Type of NPI measure. Start Date: Date the NPI was first started. End Date: Date the NPI was first lifted. Start Link: Source link of the start date. End Link: Source link of the end date. Start Notes: Contains tags that apply to both dates, and the start date individually. Tag description can be found in Table 3. This also includes notes on nuances not included in the tags. End Notes. Contains tags that only apply to the end date go here. Tag description can be found in Table 3. This also includes notes on nuances not included in the tags. Validated (Correctness): Binary field indicating if the record was validated for correctnes