Which technology behind signlanguagehub.tech ? (1) 🧠We use Convolutional Neural Networks (CNNs) to solve the problem of Image Classification, a common technique in image processing. CNNs work by automatically learning features from input images through convolutional layers. These features include basic elements like edges , corners , lines , and more complex patterns as the network goes deeper . 📍In sign language technology, CNNs are often combined with other advanced techniques like: 1. Pose Estimation: This identifies the position and movement of key body parts, such as hands and fingers, which are crucial for understanding gestures 2, Recurrent Neural Networks (RNNs) or Transformers: These are used to analyze sequences of gestures, as sign language is highly contextual and temporal. 3. Preprocessing and Augmentation: Image preprocessing techniques help normalize input data, and data augmentation generates variations of the training data to improve the model's robustness. ✔️This combination allows the technology to translate sign language into text or speech effectively, bridging communication gaps. 📎 Read more at our White Paper docs.signlanguagehub.tech/te…

A nice day to go for a walk and learn sign language at signlanguagehub.tech/learn @SignLanguageHub

Which technology behind signlanguagehub.tech ? (1) 🧠We use Convolutional Neural Networks (CNNs) to solve the problem of Image Classification, a common technique in image processing. CNNs work by automatically learning features from input images through convolutional layers. These features include basic elements like edges , corners , lines , and more complex patterns as the network goes deeper . 📍In sign language technology, CNNs are often combined with other advanced techniques like: 1. Pose Estimation: This identifies the position and movement of key body parts, such as hands and fingers, which are crucial for understanding gestures 2, Recurrent Neural Networks (RNNs) or Transformers: These are used to analyze sequences of gestures, as sign language is highly contextual and temporal. 3. Preprocessing and Augmentation: Image preprocessing techniques help normalize input data, and data augmentation generates variations of the training data to improve the model's robustness. ✔️This combination allows the technology to translate sign language into text or speech effectively, bridging communication gaps. 📎 Read more at our White Paper docs.signlanguagehub.tech/te…

The time is approaching, are you ready?

Surprise number one, To the average person these numbers may not mean much, but it is a sign that the CNN's have been trained successfully to a spread between 98 - 99% accuracy. The below picture is coming in at 98%, huge leaps and bounds are happening here. The question is, do you wanna help change the world? More surprises are on the way, stay tuned.

My Christmas gift 🎁🎄✨

19th training results of SLHUB model train/box_loss: Decreases steadily over epochs, indicating better bounding box predictions. 📔Read more: docs.signlanguagehub.tech/ar…

This is worthy of a quote. Make sure you show this jolly soul some love for the holidays.

The @grok summary for $SLHUB. This is a wonderful humanitarian project that is truly innovating for the betterment of the language barriers often surrounding the deaf Community. @SignLanguageHub ❤️ 🤗

GM FAM ❤️

We are working on that gold checkmark now. Because we fully believe in the mission we've laid out.

I highly recommend checking out @SignLanguageHub's website, since lots of the tech is already up and running. I'm a linguist by trade, and I always wanted to learn sign language, so I'll definitely dive into that AI online course 🫳🤏✌️ Gud tek $SLHUB signlanguagehub.tech/learnin…