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Bangladeshi Sign Language (BdSL) is a commonly used medium of communicationfor the hearing-impaired people in Bangladesh. A real-time BdSL interpreterwith no controlled lab environment has a broad social impact and an interestingavenue of research as well. Also, it is a challenging task due to the variationin different subjects (age, gender, color, etc.), complex features, andsimilarities of signs and clustered backgrounds. However, the existing datasetfor BdSL classification task is mainly built in a lab friendly setup whichlimits the application of powerful deep learning technology. In this paper, weintroduce a dataset named BdSL36 which incorporates background augmentation tomake the dataset versatile and contains over four million images belonging to36 categories. Besides, we annotate about 40,000 images with bounding boxes toutilize the potentiality of object detection algorithms. Furthermore, severalintensive experiments are performed to establish the baseline performance ofour BdSL36. Moreover, we employ beta testing of our classifiers at the userlevel to justify the possibilities of real-world application with this dataset.We believe our BdSL36 will expedite future research on practical sign letterclassification. We make the datasets and all the pre-trained models availablefor further researcher.

BdSL36: A Dataset for Bangladeshi Sign Letters Recognition