Search Results for author:
Found 13 papers, 9 papers with code
Temporal receptive field in dynamic graph learning: A comprehensive analysis
Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges.
Dynamic Graph Representation Learning with Neural Networks: A Survey
In this research area, Dynamic Graph Neural Network (DGNN) has became the state of the art approach and plethora of models have been proposed in the very recent years.
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition
Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document.
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network.
Ranked #1 on
Handwritten Text Recognition
on READ 2016
SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition
Unconstrained handwriting recognition is an essential task in document analysis.
Ranked #4 on
Handwritten Text Recognition
on READ2016(line-level)
Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition ?
This has a direct influence on the training time of such architectures, with also a direct consequence on the time required to explore various architectures.
Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network
Unconstrained handwritten text recognition is a major step in most document analysis tasks.
Ranked #5 on
Handwritten Text Recognition
on IAM(line-level)
End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network
For each text line features, a decoder module recognizes the character sequence associated, leading to the recognition of a whole paragraph.
Ranked #2 on
Handwritten Text Recognition
on READ2016(line-level)
Fast object detection in compressed JPEG Images
Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications.
Neural Networks Regularization Through Class-wise Invariant Representation Learning
In this work, we tackle the issue of training neural networks for classification task when few training samples are available.
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction
Offline handwritten text line recognition is a hard task that requires both an efficient optical character recognizer and language model.
Handwriting recognition using Cohort of LSTM and lexicon verification with extremely large lexicon
State-of-the-art methods for handwriting recognition are based on Long Short Term Memory (LSTM) recurrent neural networks (RNN), which now provides very impressive character recognition performance.
Deep Neural Networks Regularization for Structured Output Prediction
The motivation of this work is to learn the output dependencies that may lie in the output data in order to improve the prediction accuracy.
