{
  "id": 440701,
  "title": "A working concept as a Hand Pattern Recognition",
  "url": "/competitions/asl-fingerspelling/discussion/440701",
  "author_name": "Tags",
  "post_date": "2023-09-15T21:23:57.341000",
  "votes": 0,
  "comment_count": 0,
  "views": 0,
  "content": "<p>If this works, we'll have a powerful hand gesture recognition system. Imagine creating any hand shape and assigning it a corresponding letter!  We could recognize Vulcan greetings, rock-and-roll devil horns, binary codes with fingers, and even Naruto hand signs.</p>\n<p>However, instead of a massive list of hand shapes, I propose focusing on a  high number of well-defined and distinct hand postures. These postures would then be assigned unique identifiers (UIDs) by a classifier. Finally, we could build a module that takes an input image of a hand gesture and outputs its corresponding UID. This UID can then be mapped to any desired value, like a letter.</p>\n<p>This approach offers several advantages:</p>\n<p>Scalability: It's easier to add new gestures in the future by simply defining their UIDs and mapping them to values.<br>\nEfficiency: The classifier only needs to learn a manageable set of distinct hand shapes.<br>\nFlexibility: You can assign any value (letter, action, etc.) to the output UID.</p>",
  "messages": [
    {
      "id": 2440968,
      "postDate": "2023-09-15T21:23:57.343Z",
      "content": "<p>If this works, we'll have a powerful hand gesture recognition system. Imagine creating any hand shape and assigning it a corresponding letter!  We could recognize Vulcan greetings, rock-and-roll devil horns, binary codes with fingers, and even Naruto hand signs.</p>\n<p>However, instead of a massive list of hand shapes, I propose focusing on a  high number of well-defined and distinct hand postures. These postures would then be assigned unique identifiers (UIDs) by a classifier. Finally, we could build a module that takes an input image of a hand gesture and outputs its corresponding UID. This UID can then be mapped to any desired value, like a letter.</p>\n<p>This approach offers several advantages:</p>\n<p>Scalability: It's easier to add new gestures in the future by simply defining their UIDs and mapping them to values.<br>\nEfficiency: The classifier only needs to learn a manageable set of distinct hand shapes.<br>\nFlexibility: You can assign any value (letter, action, etc.) to the output UID.</p>",
      "rawMarkdown": "If this works, we'll have a powerful hand gesture recognition system. Imagine creating any hand shape and assigning it a corresponding letter!  We could recognize Vulcan greetings, rock-and-roll devil horns, binary codes with fingers, and even Naruto hand signs.\n\nHowever, instead of a massive list of hand shapes, I propose focusing on a  high number of well-defined and distinct hand postures. These postures would then be assigned unique identifiers (UIDs) by a classifier. Finally, we could build a module that takes an input image of a hand gesture and outputs its corresponding UID. This UID can then be mapped to any desired value, like a letter.\n\nThis approach offers several advantages:\n\nScalability: It's easier to add new gestures in the future by simply defining their UIDs and mapping them to values.\nEfficiency: The classifier only needs to learn a manageable set of distinct hand shapes.\nFlexibility: You can assign any value (letter, action, etc.) to the output UID.\n"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2440968": "If this works, we'll have a powerful hand gesture recognition system. Imagine creating any hand shape and assigning it a corresponding letter!  We could recognize Vulcan greetings, rock-and-roll devil horns, binary codes with fingers, and even Naruto hand signs.\n\nHowever, instead of a massive list of hand shapes, I propose focusing on a  high number of well-defined and distinct hand postures. These postures would then be assigned unique identifiers (UIDs) by a classifier. Finally, we could build a module that takes an input image of a hand gesture and outputs its corresponding UID. This UID can then be mapped to any desired value, like a letter.\n\nThis approach offers several advantages:\n\nScalability: It's easier to add new gestures in the future by simply defining their UIDs and mapping them to values.\nEfficiency: The classifier only needs to learn a manageable set of distinct hand shapes.\nFlexibility: You can assign any value (letter, action, etc.) to the output UID.\n"
  }
}