Handwriting Recognition Edit Task
Computer Vision • Optical Character Recognition
About
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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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 #84 on
Image Classification
on CIFAR-100
Fine-tuning Handwriting Recognition systems with Temporal Dropout
• 0x454447415244/HandwritingRecognitionSystem
• 
This paper introduces a novel method to fine-tune handwriting recognition systems based on Recurrent Neural Networks (RNN).
Handwriting Recognition of Historical Documents with few labeled data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.
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold
CVPR 2020
• IntuitionMachines/OrigamiNet
•
On IAM we even surpass single line methods that use accurate localization information during training.
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
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.