Text extraction using pytorch
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Updated
Mar 31, 2024 - Jupyter Notebook
Text extraction using pytorch
Machine learning models.
Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various encoder-decoder configurations to optimize performance metrics like MSE, SSIM, and PSNR, aiming to achieve near-lossless data compression.
# OCR with EAST Text Detection , contour and EMNIST Character Recognition
Convert typed text to realistic handwriting!
Digits Recognizer using correlation and similarity methods in MNIST Letters dataset.
A deep learning model deployed as a web app to classify handwritten digits and letters using the EMNIST dataset
This is the code for my IB Extended Essay in Computer Science
Evaluating the accuracy with different CNN kernel sizes for the EMNIST Balanced dataset
A simple NN word recognizer based on the EMNIST dataset
User handwriting recognition app using a CNN trained on the EMNIST ByClass dataset
This project aims to implement AI-driven letter recognition using neural network libraries and the EMNIST dataset. By employing deep learning and data preprocessing, we seek to build a versatile system for accurately identifying letters in both handwritten and printed text, with applications in OCR and document digitization.
Library to read the EMNIST and MNIST data sets
TextToHandwriting tool
2020/2021 sem 2 - Neural Network Individual Assignment Project - EMNIST prediction - Predict and evaluate the output of model trained using multiple MLP model created by using the EMNIST datasets.
Dask-parallelized project, contrasting GaussianNB and LightGBM models for EMNIST handwritten character classification.
Alphabet recognition using EMNIST dataset for humans ⚓
This is a simple app to predict the alphabet that is written on the screen using an object of interest.
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