Handwriting Recognition Edit Task
Computer Vision • Optical Character Recognition
About
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
Datasets
Greatest papers with code
Unsupervised learning with sparse space-and-time autoencoders
• facebookresearch/SparseConvNet
• 
We use spatially-sparse two, three and four dimensional convolutional autoencoder networks to model sparse structures in 2D space, 3D space, and 3+1=4 dimensional space-time.
Spatially-sparse convolutional neural networks
• facebookresearch/SparseConvNet
•
Convolutional neural networks (CNNs) perform well on problems such as handwriting recognition and image classification.
Ranked #50 on
Image Classification
on CIFAR-100
Handwriting Recognition of Historical Documents with few labeled data
• 0x454447415244/HandwritingRecognitionSystem
• 
In this work, we demonstrate how to train an HTR system with few labeled data.
Speech Recognition with Deep Recurrent Neural Networks
Recurrent neural networks (RNNs) are a powerful model for sequential data.
Ranked #15 on
Speech Recognition
on TIMIT
Multi-Dimensional Recurrent Neural Networks
• philipperemy/tensorflow-multi-dimensional-lstm
•
Recurrent neural networks (RNNs) have proved effective at one dimensional sequence learning tasks, such as speech and online handwriting recognition.
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
CVPR 2020
• amzn/convolutional-handwriting-gan
•
This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.
LSTM: A Search Space Odyssey
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold
CVPR 2020
• IntuitionMachines/OrigamiNet
•
Text recognition is a major computer vision task with a big set of associated challenges.
Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
• IntuitionMachines/OrigamiNet
•
Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges.
HANDWRITING RECOGNITION LICENSE PLATE RECOGNITION SCENE TEXT SCENE TEXT RECOGNITION
Start, Follow, Read: End-to-End Full-Page Handwriting Recognition
ECCV 2018
• cwig/start_follow_read
•
Despite decades of research, offline handwriting recognition (HWR) of degraded historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives.