Multiple Handwritten Digit Recognition app Using Deep Learing - CNN from Canvas build on tkinter- GUI

Handwritten Digit Recognition using OpenCV, Keras, CNN

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.

[Keras|OpenCV|Flask] Implemented a numerical calculator by digit and symbol recognition on handwritten images. Build and trained a CNN model on Kaggle’s Handwritten math symbols Dataset using Keras in Python and OpenCV for pre-processing the input image. The model can recognize digits, arithmetic and relational operators.

This project focuses on recognising string of handwritten digits inputed as an image

A Handwritten Equation Solver built using Convolutional Neural Network and Optical Character Recognition. CNN model is used for recognition of digits and symbols. OCR is used for processing the the image and segmentation.

MNIST Handwritten Digit Classification and Recognition Using Convolutional Neural Network (CNN) Deep Learning

Flask web app for realtime handwritten digit recognition using CNN model (MNIST dataset).

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.

This is demo app that efficiently solves the handwritten digit recognition problem, using Convolutional Neural Network (CNN).

Handwritten Digit Recognition is a deep learning project that uses a Convolutional Neural Network (CNN) to accurately identify digits (0–9) from handwritten images. It leverages the MNIST dataset to train and evaluate the model for real-time digit classification.

This project demonstrates Handwritten-Digit-Recognition using (CNN) Convolutional Neural Networks.

Deployable App to test different Neural Network architectures for handwritten digit recognition under adverse conditions using CNNs trained on the MNIST dataset

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This project implements and explains Python code to recognize handwritten digits (MNIST dataset) with a CNN using Keras.

HANDWRITTEN DIGIT RECOGNITION USING CNN

Build an accurate digit recognition model using PyTorch. Train a deep learning CNN on the MNIST dataset to classify handwritten digits. GPU acceleration for faster training. Ideal for image recognition enthusiasts.

CNN EMNIST Visualization is a deep learning project that trains CNN models on MNIST digits and EMNIST letters. It includes a Streamlit web app for drawing characters and viewing real-time predictions, making it ideal for learning and demonstrating handwritten recognition using CNNs.

Production-ready deep learning model for handwritten digit recognition using optimized CNN architecture. Features interactive web app, comprehensive training pipeline, and multiple deployment options. Achieves 99.65% accuracy on MNIST dataset with advanced data augmentation and regularization techniques.

Handwritten digit recognition using CNN

Handwritten Digit Recognition using CNN in Python with Keras

Convolutional Neural Network is used for handwritten digit recognition. The standard MNIST data set is used along with the MATLAB CNN Toolbox

✍️ Handwritten Text Recognition (HTR) This repository contains a deep learning-based system for Handwritten Text Recognition (HTR) using CNN architectures such as LeNet-5 and AlexNet, trained on the EMNIST dataset. The system classifies grayscale images of individual handwritten characters or digits using PyTorch/Keras.

This project is a web-based handwritten digit recognition app using a CNN model built with Keras and TensorFlow, deployed with Flask. Users draw digits in the browser, which the model classifies in real-time. With a high accuracy , it offers an interactive, accurate interface for digit recognition.

Built a Convolutional Neural Network from first principles using NumPy to understand internals (conv, pooling, softmax). Trained a high-accuracy CNN using TensorFlow and integrated it into a Streamlit app for real-time handwritten digit recognition.

The objective of this project is to recognize handwritten digits based on real time image acquisition from a camera. Currently, several algorithms are suitable for this classification task, such as Bag of Words approach, Decision Trees or Artificial Neural Networks (ANN). However, the most powerful image recognition tools are based on Convolutional Neural Network (CNN), which is going to be the approach chosen for this project. For training and testing MNIST dataset was used. The final net achieved a 98.54% train accuracy and 98.40% test accuracy. For real time segmentation, blob analysis was carried out, and for digit preprocessing, resize and Gauss filter were used to adequate each detected digit to CNN input. The final model was tested in a 1280x720 Webcam with sample time of 1 sec.

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A machine learning project that implements handwritten digit recognition using a Convolutional Neural Network (CNN). The system is trained on the MNIST dataset to accurately classify digits (0–9) from images, demonstrating the power of deep learning in image recognition tasks.

This project implements a Convolutional Neural Network (CNN) for recognizing handwritten digits using the MNIST dataset. It demonstrates the complete machine learning pipeline — from data preprocessing and model training to evaluation and prediction — and serves as a foundation for more advanced image recognition projects.

This Keras code builds and trains a CNN for MNIST digit classification. The model includes three convolutional layers, each followed by max-pooling, and two dense layers. It achieves 99.00% accuracy on the test set. The dataset is normalized and labels are one-hot encoded.