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This project offers an efficient method for identifying and recognizing handwritten text from images. Using a Convolutional Recurrent Neural Network (CRNN) for Optical Character Recognition (OCR), it effectively extracts text from images, aiding in the digitization of handwritten documents and automated text extraction.
Keras Fully Connected Neural Network using Python for Digit Recognition
Handwritten Digit Recognition using Softmax Regression in Python
Deep learning principle practice, based on MNIST hadwritten digit recognition
Hello World of Computer Vision and ML
Using a Convolutional Neural Network (CNN) to identify the Kannada numerical digits. Tensorflow (Keras) is used to create, train and load the neural network model. CustomTKinter/TKinter are used to provide the GUI and OpenCV is used to read input form the GUI.
MNIST数据集 手写数字识别 CNN
This contains the assignment solutions for the course CMPT 762 Computer Vision @ SFU
This project uses TinyVGG and Streamlit to classify handwritten digits.
A basic neural network built from scratch using C++
This project implements a CNN for handwritten digit classification on the MNIST dataset using PyTorch. It uses stacked convolutional layers with dropout, batch normalization, and max pooling to classify 28×28 grayscale digits (0–9) with Softmax output.
Implementing computer vision fundamentals with the famous MNIST data.
Implemented various Machine Learning and Deep Learning Algorithms on the famous digit recognition problem using the MNIST (Mixed National Institute of Standards and Technology) database.
A Convolutional Neural Network to predict handwriting ditgit form image by MNIST datasets
An implementation of Neural Networks from scratch in python using only numpy for MNIST dataset .
Train a CNN on MNIST and classify digits drawn in Paint
Digit recognition using neural networks(CNN classification technique)
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