chaitut715

chaitut715's Stars

• axioncloud/DigitalBadgesAPSU

  Open Badges implementation for CSCI 5630

  Language:PHP1

• UOC/spring-boot-lti-advantage

  Language:Java1416

• UOC/java-lti-1.3

  Language:Java2211

• deviantony/docker-elk

  The Elastic stack (ELK) powered by Docker and Compose.

  Language:Shell17.1k6.7k

• instructure/canvas-android

  Canvas Android apps

  Language:Kotlin12499

• ollide/spring-discourse-sso-boot

  Discourse SSO for Spring Boot

  Language:Java12

• indexdata/cql-java

  CQL parser for Java

  Language:Java125

• ozgurural/MS-Thesis

  My Master of Science Thesis Project related researches, projects and documents.

  Language:HTML3

• aritter/twitter_download

  Download scripts for distributing twitter data.

  Language:Python6245

• das-lab/Cyberthreat-Detection

  This is a dataset for cyberthreat detection research

  2

• JuanCasado/Hadoop-Docker

  Hadoop deployment on docker and Docker Swarm

  Language:TSQL31

• Ziyu0/twitter-sentiment-analysis-using-ELK-stack-and-python

  :airplane: A simple pipeline to retrieve, analyze and visualize Tweets in real time with Elasticsearch and Python

  Language:Python94

• azxkenzo/InsDownloader

  Instagram Pictures Downloader In Android !

  Language:Kotlin82

• ghoshavirup0/Senior_Saviour

  The project is to develop an intelligent health and hazard monitoring system for the elderly people within our supervision.

  Language:Java11

• rob729/Notification

  Language:Kotlin1511

• sergiandreplace/flutter_planets_tutorial

  The Flutter Planets app tutorial with commits per lesson

  Language:Dart755174

• apache/hbase

  Apache HBase

  Language:Java5.2k3.3k

• vi3k6i5/flashtext

  Extract Keywords from sentence or Replace keywords in sentences.

  Language:Python5.6k598

• alexvlis/denoising-autoencoder

  Denoising Autoencoder on MNIST implemented in Tensorflow

  Language:Jupyter Notebook74

• NanoNets/ocr-with-tesseract

  A comprehensive tutorial for OCR in python using Tesseract-OCR and OpenCV

  Language:Jupyter Notebook11870

• bharathbhimshetty/Denoising-Dirty-Documents

  # 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.

  Language:Jupyter Notebook97

• faustomorales/keras-ocr

  A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.

  Language:Python1.4k356

• shivamgupta7/OCR-Handwriting-Recognition

  Using TensorFlow to create a ResNet model to train a deep learning model for images. Using OpenCV to do some image processing and show image with boundary box.

  Language:Python258

• databricks/LearningSparkV2

  This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]

  Language:Scala1.2k722

• behzadanksu/cybertweets

  annotated dataset of cyber-security related tweets

  Language:HTML2012

• sridharswamy/Twitter-Sentiment-Analysis-Using-Spark-Streaming-And-Kafka

  Twitter Sentiment Analysis using Spark and Kafka

  Language:Python11374

• ndionysus/twitter-cyberthreat-detection

  This repository holds the dataset used to conduct experiments for the "Cyberthreat Detection from Twitter using Deep Neural Networks" accepted to the IJCNN 2019.

  Language:Python3012

• anubhavsaxena14/Threat-Detection-using-Sentiment-Analysis

  Threat Detection System using Hybrid (Machine Learning + Lexical Analysis) learning Approach.

  Language:Python105

• martinpella/twitter-airlines

  Sentiment Analysis on tweets from US airlines customers

  Language:Jupyter Notebook145

• steven-matison/dfhz_hdp_mpack

  Install Ambari 2.7.5 with HDP 3.1.4 without using Hortonworks repositories.

  Language:Python4838