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Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation
Performances of Handwritten Text Recognition (HTR) models are largely determined by the availability of labeled and representative training samples.
Recognition-free Question Answering on Handwritten Document Collections
In recent years, considerable progress has been made in the research area of Question Answering (QA) on document images.
Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling
Word spotting is a popular tool for supporting the first exploration of historic, handwritten document collections.
Exploring Confidence Measures for Word Spotting in Heterogeneous Datasets
In recent years, convolutional neural networks (CNNs) took over the field of document analysis and they became the predominant model for word spotting.
Exploring Architectures for CNN-Based Word Spotting
The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query.
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