Demonstration of different algorithms and operations on faces. Star the repo⭐
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition (ICDAR 2023)
It is an Integrated Solution that allows you to have thorough attendance review and eliminate duplicate data entry and errors in time and attendance entries. This application is user-friendly and flexible which helps you to track the attendance of the students effectively.
The C++ neural network for handwritten digit recognition with online demo
Live Human Activity recognition using Tensorflow transfer learning model, OpenCV and numpy with a custom Dataset by scraping the web.
This project was my final Bachelor's degree thesis. In it I decided to mix my passion, music, and the syllabus that I liked the most in my degree, deep learning.
automatic license plate recognition system.
It's an app built in Flutter that allows you to recognize the kana (hiragana and katakana) drawn in the app.
Object recognition by random binary data lookup for Oracle MNIST
Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues.
Organização PlayCultural Bem-vindo ao repositório da Organização PlayCultural! Aqui você encontrará projetos, ferramentas e recursos desenvolvidos para impulsionar a inovação e a excelência na produção de eventos culturais, esportivos e corporativos.
Object recognition by random binary data lookup for QMNIST
Object recognition by random binary data lookup for Fashion MNIST
Object recognition by random binary data lookup proof of concept for Fashion MNIST
Object recognition by random binary data lookup proof of concept for QMNIST
Work area recognition for small robots. Computer Vision Research Internship. TUAT, Japan.
Below are the prerequisites for this project: Python (3.7.4 used) IDE (Jupyter used) Required frameworks are Numpy (version 1.16.5) cv2 (openCV) (version 3.4.2) Keras (version 2.3.1) Tensorflow (Keras uses TensorFlow in backend and for some image preprocessing) (version 2.0.0) Matplotlib (version 3.1.1) Pandas (version 0.25.1)
Hand recognition system developed on YOLOv4.
A collection of open-source, educational MVPs for learning and development. Explore hands-on examples in AI, machine learning, web development, IoT, and more. Designed for students, developers, and educators, this repository encourages experimentation and practical learning. Contributions are welcome!
The goal is to recognize fruit through machine learning algorithm implementations such as K-NN algorithm or K-Mean algorithm.
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