Handwriting Recognition
49 papers with code • 3 benchmarks • 20 datasets
Libraries
Use these libraries to find Handwriting Recognition models and implementationsMost implemented papers
Digital Peter: Dataset, Competition and Handwriting Recognition Methods
This paper presents a new dataset of Peter the Great's manuscripts and describes a segmentation procedure that converts initial images of documents into the lines.
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
The architecture is not limited to handwriting recognition, but is generally applicable as an extremely fast pattern recognition processor for various kinds of patterns such as speech and images.
Drawing and Recognizing Chinese Characters with Recurrent Neural Network
In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters.
A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation
We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text.
The Impact of Random Models on Clustering Similarity
It is often argued that, in order to establish a baseline, clustering similarity should be assessed in the context of a random ensemble of clusterings.
Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens
The second model used a 1D ConvNet architecture but was applied to the sequence of polar vectors connecting the touch points.
Start, Follow, Read: End-to-End Full-Page Handwriting Recognition
Despite decades of research, offline handwriting recognition (HWR) of degraded historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives.
Handwriting Recognition of Historical Documents with few labeled data
In this work, we demonstrate how to train an HTR system with few labeled data.
Unsupervised learning with sparse space-and-time autoencoders
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.
Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges.