sign language recognition

Sign language correctness discrimination (SLCD) dataset is collected for sign language teaching. Different from general sign language recognition datasets, SLCD dataset has two kind labels of sign language category and standardization category at the same time. The standardization category is to describe action correctness of the same sign language made by students. The SLCD dataset videos in this paper are obtained by camera. 76 students are recruited to collect sign language actions.

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N-WLASL dataset is a synthetic event-based dataset comprising 21,093 samples across 2,000 glosses. The dataset was collected using an event camera to shoot toward an LCD monitor. The monitor plays video frames from WLASL, the largest public word-level American Sign Language dataset. We use the event camera DAVIS346 with a resolution of 346x260 to record the display. The video resolution of WLASL is 256x256 and the frame rate is 25Hz. To ensure accurate recording of the display, we have implemented three video pre-processing procedures using the python-opencv and dv packages in Python.

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Sign languages are natural, gestural languages that use visual channel to communicate. Deaf people develop them to overcome their inability to communicate orally. Sign language interpreters bridge the gap that deaf people face in society and provide them with an equal opportunity to thrive in all environments. However, Deaf people often struggle to communicate on a daily basis, especially in public service spaces such as hospitals, post offices, and municipal buildings.

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The dataset contains medical signs of the sign language including different modalities of color frames, depth frames, infrared frames, body index frames, mapped color body on depth scale, and 2D/3D skeleton information in color and depth scales and camera space. The language level of the signs is mostly Word and 55 signs are performed by 16 persons two times (55x16x2=1760 performance in total).

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Our Signing in the Wild dataset consists of various videos harvested from YouTube containing people signing in various sign languages and doing so in diverse settings, environments, under complex signer and camera motion, and even group signing. This dataset is intended to be used for sign language detection.

 

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