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followers: 25.0 following: 16.0 repos: 37.0 gists: 0.0

Name: Ibna Kowsar

Type: User

Company: TSU

Bio: CS MS student @ TSU

Location: Nashville, TN

Blog: https://kawseribn.github.io/

Hello there! 👋

I'm actively engaged in several exciting areas in the tech field. Here’s a snapshot of what I'm working on and my interests:

🔭 Current Projects: I'm focusing on Domain Adaptation and Missing Data Imputation techniques.
🌱 Learning: I'm currently deepening my knowledge in Deep Learning, Computer Vision, and Data Science.
👯 Collaboration: I'm eager to collaborate on innovative research projects, particularly in Health Informatics and Computer Vision.

Connect with Me:

Feel free to reach out if you are interested in discussing potential collaborations or just want to connect!

Ibna Kowsar's Projects

accessing-and-modifying-different-layers-of-a-pretrained-model-in-pytorch

careers-resume-technical-experience

coding-interview-university

A complete computer science study plan to become a software engineer.

comp5400_project

A SQL based project focusing on DDL and DML methodologies.

compactfer

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.

doleetcode

driver-drowsiness-detection

facial_expression_recognition

Facial Expression Recognition by Convolutional Neural Network(Keras), real time recognition with OpenCV, Final Project for Deep Learning Fall 2018@GWU