handwritten digit recognition system using a custom neural network architecture. Built with C, it features both training capabilities and an interactive recognition interface. >98% accuracy on the MNIST dataset.

Andrew Ng's Machine Learning Class Projects Description: Ex1 - Gradient Descent, Newton's Method, Linear Regression Ex2 - Sigmoid Kernels Ex3 - Logistic Regression Implementation Ex4 - Neural Networks implementation for Digit Recognition Ex5 - Regularized Linear Regression, Polynomial Regression Ex6 - SVM (Kernel implementation) for Spam Classification Ex8 - Recommender System (Collaborative Filtering) and Anomaly Detection

10 digits recognition system based on DTW, HMM and GMM

FPGA implementation of a handwritten digit recognition system based on k-nearest-neighbors (k-NN) classifier algorithm.

A complete handwritten digit recognition system using Scikit-learn on the MNIST dataset, featuring model training, evaluation, error analysis, and Gradio app deployment.

Simple automatic speech recognition system based on digits corpora (Polish language), created in Kaldi toolkit. Despite of the language difference, this is an effect of 'Kaldi for dummies' tutorial published in kaldi-help discussion group. No audio data - this is just an example.

This project is a handwriting recognition system using a convolutional neural network (CNN) based on TensorFlow and Keras. It can recognise handwritten digits and also allows users to draw digits on a canvas for real-time recognition.

✋In the Hand Gesture Recognition System project, we aim to develop a comprehensive machine learning pipeline capable of accurately classifying hand gestures into six digits (0 to 5). Our system is designed to handle variations in lighting effects and hand poses, ensuring robust performance in real-world scenarios.

Hand-written digit recognition system for Python.

It is Described as: 1) —Digital documents are easier to store and process. The task of taking a decision to identify the character can be accomplished by using Recognition is to make editable documents from the existing paper documents or image files by employing automatic classification methods to so that various operations can be implemented on the document with ease. 2)-It reduces human efforts to a large extent, making work more reliable and time-efficient. A research was taken on to identify the gaps in the existing methodologies to come up with a solution. Recognito identifies isolated integral values by making use of Convolution Neural Network and Supervised Learning algorithms. 3)-It learns from the regular training of database by inputs provided to it and, thus increasing the reliability and accuracy gradually.

Handwritten Digit Recognition with Deep Learning This project demonstrates a real-time handwritten digit recognition system using a Convolutional Neural Network (CNN), trained on the popular MNIST dataset and deployed using Pygame.

Linear regression, logistic regression, polynomial regression, multiclass classification, neural networks, KMeans, Principle Component Analysis (PCA), and Support Vector Machine (SVM). Fun machine learning applications: hand-written digit recognition model, spam email filter, image compression, anomaly detection model, and movie recommendation system.

Advanced Digit Recognition System using a Hybrid Modern-CNN (ResNet + ConvNeXt) & Ensemble Inference Engine. Features a robust OpenCV preprocessing pipeline and FastAPI backend.

Currency Recognition System is a Digital Image Processing Software in which input is scanned currency note. It processes the image and gives output whether the currency is fake or genuine and which country it belongs to.It is developed with MATLAB software.

System for voice processing and recognition, especially for isolated digits in English. Recognition is done with Gaussian Mixture Models – Hidden Markov Models and also a Recurrent Neural Network is implemented with Pytorch.

An efficient system for recognition of handwritten digits, making use of various image processing techniques. Experimented with 6 Machine Learning algorithms.

🧠 OCR Neural Network A handwritten digit recognition system built from scratch using a simple feedforward neural network. Accepts 28×28 pixel grayscale inputs and classifies them into digits (0–9). Includes training, prediction, and a web-based drawing canvas.

This project is a digit recognition system using a neural network model trained on the MNIST dataset. Users can draw digits in a PyGame window, and the model will recognize the drawn digit. This project was created as a learning exercise to apply concepts from an intro to deep learning course and to incorporate an interactive element to the models

This project focuses on building an image denoising system using autoencoders. The autoencoder is trained to remove noise from the MNIST dataset images, which are commonly used for digit recognition tasks. The project includes several advanced features such as data augmentation, model checkpointing, learning rate scheduling, and comprehensive eval.

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This work is for video of diode dial digital recognition.

A Digit recognition system that is recognizing the digits using deep learning from the dataset that comes from different sources like emails, bank cheque, papers, images, etc. and in different real-world scenarios for online handwriting recognition on computer tablets or system, recognize number plates of vehicles, processing bank cheque amounts, numeric entries in forms filled up by hand.

信号与系统课程设计：基于 mfcc 特征，vad 算法和机器学习的连续数字 语音识别系统

A Novel Method for Handwritten Digit Recognition System

Based on CNN and utilises the PyTorch and Flask frameworks.

Handwritten digit recognition system not only detects scanned images of handwritten digits.Handwritten digit recognition using MNIST dataset is a major project made

A speech recognition system of spoken digits using the k-means clustering algorithm.

Machine Learning models on Anomaly detection, Recommender system on movies based on IMDB dataset, Digit Identification using Logistic regression, Neural network based facial feature recognition, PCA, SVM based Spam filter, Logistic Regression - Nelder Mead

Handwritten Digit Recognition using k-NN | This project implements a handwritten digit classification system using the k-Nearest Neighbors (k-NN) algorithm on the MNIST dataset. It involves data preprocessing, model implementation, and hyperparameter tuning to achieve high accuracy in digit prediction.

System for visual digit recognition. Data come from US Postal Service (handwritten and scanned) and are digits from 0-9. Euclidean distance classifier is created in the principles of scikit-learn. Ensembling is also used, such us other known classifiers in scikit-learn.