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This project is a comprehensive solution for recognizing handwritten digits and text from images, with functionalities for training, testing, and usage, making it suitable for tasks like cheque amount verification and other handwritten text recognition applications.
Automate handwritten multiple-choice test grading with HMC-Grad, using a CNN trained in PyTorch on the EMNIST dataset and OpenCV for image processing. Input the correction key and the images of the answer sheets to receive each one's correctness and score, along with item and score analysis, in CSV and XLSX formats, and the annotated images as JPG.
🔢🖋️ ProjetCHROME - Reconnaissance de formules mathématiques manuscrites. Ce projet transforme des expressions mathématiques écrites à la main (InkML) en formats numériques. 🤖💡 Il intègre des techniques de vision par ordinateur et d'apprentissage profond pour un traitement complet : segmentation, classification et sélection de symboles. 📚🔍
Handwritten Bangla Character Classification using ResNet-34 trained using BanglaLekha Dataset. System has been implemented in PyTorch. For details, see the README file.
Handwritten character recognition (HCR) is a challenging task due to the variability of human handwriting. This repository contains a convolutional neural network (CNN) architecture for HCR that uses Keras as an interface for the TensorFlow library. The model has been validated for English and Devanagari scripts.