Ibna Kowsar's Projects
Profile Repo
Bangla Name Generator
✍️ Bengali alphabet (বাংলা বর্ণমালা).
A complete computer science study plan to become a software engineer.
A SQL based project focusing on DDL and DML methodologies.
The implementation of CVPR 2018 FER paper: A Compact Deep Learning Model for Robust Facial Expression Recognition
# Denoising Dirty Documents Optical Character Recognition (OCR) is the process of getting type or handwritten documents into a digitized format. If you've read a classic novel on a digital reading device or had your doctor pull up old healthcare records via the hospital computer system, you've probably benefited from OCR. OCR makes previously static content editable, searchable, and much easier to share. But, a lot of documents eager for digitization are being held back. Coffee stains, faded sun spots, dog-eared pages, and lot of wrinkles are keeping some printed documents offline and in the past. This competition challenges you to give these documents a machine learning makeover. Given a dataset of images of scanned text that has seen better days, you're challenged to remove the noise. Improving the ease of document enhancement will help us get that rare mathematics book on our e-reader before the next beach vacation. We've kicked off the fun with a few handy scripts to get you started on the dataset. Acknowledgements Kaggle is hosting this competition for the machine learning community to use for fun and practice. This dataset was created by RM.J. Castro-Bleda, S. España-Boquera, J. Pastor-Pellicer, F. Zamora-Martinez. We also thank the UCI machine learning repository for hosting the dataset. If you use the problem in publication, please cite: Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science ## AIM: * To Denoise the images using Encoder-Decoder Model ## Dataset: * https://www.kaggle.com/c/denoising-dirty-documents/data * We are provided two sets of images, train and test. These images contain various styles of text, to which synthetic noise has been added to simulate real-world, messy artifacts. The training set includes the test without the noise (train_cleaned). You must create an algorithm to clean the images in the test set.
Facial Expression Recognition by Convolutional Neural Network(Keras), real time recognition with OpenCV, Final Project for Deep Learning Fall 2018@GWU
An efficient Metaheuristic Approach for Finding Motifs from DNA Sequences
Git Helper
Personal solutions
generate captions for images using a CNN-RNN model that is trained on the Microsoft Common Objects in COntext (MS COCO) dataset
Facial Expression Recognition using Inception V3 Model in keras
Desktop application for tesseract.js