•    Freeware
  •    Shareware
  •    Research
  •    Localization Tools 20
  •    Publications 687
  •    Validators 2
  •    Mobile Apps 22
  •    Fonts 31
  •    Guidelines/ Draft Standards 3
  •    Documents 13
  •    General Tools 31
  •    NLP Tools 105
  •    Linguistic Resources 239
  Catalogue
Active learning and crowd sourcing are becoming increasingly popular in the machine learning community for fast and cost effective generation of labels for large volumes of data. However, such labels may be noisy. So, it becomes important to ignore the noisy labels for building of a good classifier. We propose a framework for finding the best possible augmentation of a classifier for the character recognition problem using minimum number of crowd labeled samples. The approach inherently rejects the noisy data and tries to accept a subset of correctly labeled data to maximize the classifier performance.

Added on March 27, 2018

22

  More Details
  • Contributed by : OCR Consortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Arpit Agarwal,Ritu Garg,Santanu Chaudhury
Author Community Profile :