Dear Colleagues,

The field of handwritten text recognition (HTR) technology is rapidly progressing, wherein vast amounts of ancient handwritten manuscripts in libraries and other institutions across the world are increasingly undergoing digitization and transcription. However, the automatic recognition of heavily degraded manuscripts to render them readable and searchable remains a challenging task. This is due to degradations including ink bleed-through, ink corrosion, stains on paper or parchment, etc. that limit the effectiveness of existing HTR techniques. In recent times, machine learning, and deep learning-based methods in particular, have achieved significant performance improvements in handwriting recognition. The aim of this Special Issue is to present recent advances in methods and applications for HTR, attracting research papers from a wide array of disciplines, including machine learning, pattern recognition, image analysis, and digital humanities. The focus is to highlight advances in the research topics representing HTR from a broad perspective, and to promote new algorithms and methodologies for better reflection of the state-of-the-art in this field.

We are interested in original research that addresses the various issues in handwritten text recognition. Topics include, but are not limited to, the following:

Handwritten document analysis;

Text line extraction or segmentation;

Document image binarization;

Background noise removal;

Word spotting;

Word recognition;

Automatic recognition and transcription of manuscripts;

Active learning for handwritten text recognition;

Scribe identification;

Dating of historical manuscripts;

Document layout analysis;  

Human–document interaction;

Feature extraction and representation;

Digital humanities applications;

Image processing, classification, and retrieval.

Prof. Anders Hast
Dr. Alessia Amelio
Guest Editors

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