When I brought up the point and created shortcuts in the form of Excel macros, I would suggest improvements in workflows and was often told:Īs this was outside the realm of my job description of sales, I regretfully found myself in a hole for a long time often thinking to myself: In my former profession I often would find and focus on problems, or inefficiencies in processes. Self Reflection and Why I Chose this Project If ASL can accurately be interpreted through a machine learning application, even if it starts with just the alphabet, we can mark a step in providing greater accessibility and educational resources for our deaf and hard of hearing communities. To give these members of our community a greater voice, I attempted to answer this question as my Data Science boot camp capstone project at General Assembly: Can computer vision bridge the gap for the deaf and hard of hearing by learning American Sign Language? Moreover, access to qualified interpretation services isn’t feasible in many cases, which can result in underemployment, social isolation, and public health challenges. The deaf community is often in situations where verbal communication is the norm. Simple tasks like ordering food, discussing financial matters at the bank, explaining your conditions at the hospital, or even talking to friends and family may seem daunting when they can no longer understand you. A (no sound) video of the full American English Sign Language. What would you do if you couldn’t hear anymore? What if you could only use your hands to communicate? The English alphabet in sign language, identified by computer vision. Please suggest improvements.ĭavid has open sourced his American Sign Language computer vision dataset. ![]() The post is also available on David's blog.ĭavid is actively soliciting feedback on his work (and more data) via this Google Form. David has open sourced all materials related to this project on his GitHub. The below post is a lightly edited guest post by David Lee, a data scientist using computer vision to boost tech accessibility for communities that need it.
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