Special Issue "Handwritten Text Recognition: Methods and Applications"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Document Analysis and Processing".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Prof. Anders Hast
E-Mail Website
Guest Editor
Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala, Sweden
Interests: computer vision and image processing, especially for applications in microscopy, aerial photography, face and object recognition and hand written text recognition
Special Issues and Collections in MDPI journals
Dr. Alessia Amelio
E-Mail Website
Guest Editor
Ph.D. in Computer Science Engineering and Systems, Italy
Interests: image analysis; machine learning; data science; document analysis; natural language processing

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Article
Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text
J. Imaging 2020, 6(12), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120141 - 18 Dec 2020
Viewed by 788
Abstract
This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) [...] Read more.
This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value < 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models. Full article
(This article belongs to the Special Issue Handwritten Text Recognition: Methods and Applications)
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Article
CleanPage: Fast and Clean Document and Whiteboard Capture
J. Imaging 2020, 6(10), 102; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6100102 - 01 Oct 2020
Viewed by 673
Abstract
The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this [...] Read more.
The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing, and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work, and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use. Full article
(This article belongs to the Special Issue Handwritten Text Recognition: Methods and Applications)
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