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Found 7 papers, 4 papers with code
Full Page Handwriting Recognition via Image to Sequence Extraction
We present a Neural Network based Handwritten Text Recognition (HTR) model architecture that can be trained to recognize full pages of handwritten or printed text without image segmentation.
Ranked #11 on
Handwritten Text Recognition
on IAM
Caffe: Convolutional Architecture for Fast Feature Embedding
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Anytime Recognition of Objects and Scenes
On suitable datasets, we can incorporate a semantic back-off strategy that gives maximally specific predictions for a desired level of accuracy; this provides a new view on the time course of human visual perception.
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Convolutional Neural Networks (CNNs) can provide accurate object classification.
Recognizing Image Style
The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research.
Timely Object Recognition
On the timeliness measure, our method obtains at least $11\%$ better performance.
An Additive Latent Feature Model for Transparent Object Recognition
The appearance of a transparent patch is determined in part by the refraction of a background pattern through a transparent medium: the energy from the background usually dominates the patch appearance.
