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.

Object recognition by random binary data lookup for Fashion MNIST

Object recognition by random binary data lookup for QMNIST

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

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

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

The goal is to recognize fruit through machine learning algorithm implementations such as K-NN algorithm or K-Mean algorithm.

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.

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

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