Work area recognition for small robots. Computer Vision Research Internship. TUAT, Japan.

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.

Demonstration of different algorithms and operations on faces. Star the repo⭐

This space contains the code developed for the image pattern recognition lecture

Automatic License Plate Recognition System.

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.

Object recognition by random binary data lookup for Oracle MNIST

Object recognition by random binary data lookup for Fashion MNIST

Object recognition by random binary data lookup for QMNIST

Created an ASR (Automatic Speech Recognition) system that takes in individual recordings. Each recording represents a sentence composed of 5-10 English language digits, separated by adequate pauses. The system involves segmenting the sentence using a classifier, differentiating between background and foreground sounds.

Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition (ICDAR 2023)

A Real Time Kurdish handwriting Language Number Recognition Model Using Deep Learning (AlexNet) over used website

Esse projeto utiliza uma API de reconhecimento de voz para gerar interatividade com o usuário que deve acertar o nome da cor sorteada pelo sistema. Projeto ainda em processo de construção.

Esse jogo utiliza uma Api de reconhecimento de voz para gerar interatividade com o usuário que deve acertar um numero aleatório sorteado pelo sistema. Jogo feito por Vinícius Dias Rodrigues

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.

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)

Object recognition by random binary data lookup proof of concept for QMNIST

Object recognition by random binary data lookup proof of concept for Fashion MNIST

It's an app built in Flutter that allows you to recognize the kana (hiragana and katakana) drawn in the app.