119 citations · 552 across the 37 of their papers we have counts for
5 papers · 1 filter
Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines
Andreas Kölsch, Muhammad Zeshan Afzal, Markus Ebbecke +1
This paper presents an approach for real-time training and testing for document image classification. In production environments, it is crucial to perform accurate and (time-)effic…
AirScript - Creating Documents in Air
Ayushman Dash, Amit Sahu, Rajveer Shringi +5
This paper presents a novel approach, called AirScript, for creating, recognizing and visualizing documents in air. We present a novel algorithm, called 2-DifViz, that converts the…
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
Muhammad Zeshan Afzal, Andreas Kölsch, Sheraz Ahmed +1
We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error b…
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
Ayushman Dash, John Cristian Borges Gamboa, Sheraz Ahmed +2
In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing i…
Multilevel Context Representation for Improving Object Recognition
Andreas Kölsch, Muhammad Zeshan Afzal, Marcus Liwicki
In this work, we propose the combined usage of low- and high-level blocks of convolutional neural networks (CNNs) for improving object recognition. While recent research focused on…