74 citations · 198 across the 11 of their papers we have counts for
13 papers
Weakly Supervised Scene Text Detection using Deep Reinforcement Learning
Emanuel Metzenthin, Christian Bartz, Christoph Meinel
The challenging field of scene text detection requires complex data annotation, which is time-consuming and expensive. Techniques, such as weak supervision, can reduce the amount o…
Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data
Christian Bartz, Hendrik Raetz, Jona Otholt +2
One of the most pressing problems in the automated analysis of historical documents is the availability of annotated training data. The problem is that labeling samples is a time-c…
Handwriting Classification for the Analysis of Art-Historical Documents
Christian Bartz, Hendrik Rätz, Christoph Meinel
Digitized archives contain and preserve the knowledge of generations of scholars in millions of documents. The size of these archives calls for automatic analysis since a manual an…
One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN
Christian Bartz, Joseph Bethge, Haojin Yang +1
Generative Adversarial Networks (GANs) have achieved state-of-the-art performance for several image generation and manipulation tasks. Different works have improved the limited und…
MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?
Joseph Bethge, Christian Bartz, Haojin Yang +2
Binary Neural Networks (BNNs) are neural networks which use binary weights and activations instead of the typical 32-bit floating point values. They have reduced model sizes and al…
KISS: Keeping It Simple for Scene Text Recognition
Christian Bartz, Joseph Bethge, Haojin Yang +1
Over the past few years, several new methods for scene text recognition have been proposed. Most of these methods propose novel building blocks for neural networks. These novel bui…
LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks
Christian Bartz, Haojin Yang, Joseph Bethge +1
Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availabil…
Learning to Train a Binary Neural Network
Joseph Bethge, Haojin Yang, Christian Bartz +1
Convolutional neural networks have achieved astonishing results in different application areas. Various methods which allow us to use these models on mobile and embedded devices ha…
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Christian Bartz, Haojin Yang, Christoph Meinel
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the t…
Language Identification Using Deep Convolutional Recurrent Neural Networks
Christian Bartz, Tom Herold, Haojin Yang +1
Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks,…
STN-OCR: A single Neural Network for Text Detection and Text Recognition
Christian Bartz, Haojin Yang, Christoph Meinel
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In re- cent years several new systems that try to solve at least one of the…
BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet
Haojin Yang, Martin Fritzsche, Christian Bartz +1
Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations. Therefore it could signific…
Image Captioning with Deep Bidirectional LSTMs
Cheng Wang, Haojin Yang, Christian Bartz +1
This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN…