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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.
This paper introduces a novel method to fine-tune handwriting recognition systems based on Recurrent Neural Networks (RNN).
In this work, we demonstrate how to train an HTR system with few labeled data.
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.
On IAM we even surpass single line methods that use accurate localization information during training.
Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges.