research-article

Ground truth creation for handwriting recognition in historical documents

Authors:

Andreas Fischer,

Emanuel Indermühle,

Gabriel Viehhauser,

Michael StolzAuthors Info & Claims

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

Pages 3 - 10

https://doi.org/10.1145/1815330.1815331

Published: 09 June 2010 Publication History

Abstract

Handwriting recognition in historical documents is vital for the creation of digital libraries. The creation of readily available ground truth data plays a central role for the development of new recognition technologies. For historical documents, ground truth creation is more difficult and time-consuming when compared with modern documents. In this paper, we present a semi-automatic ground truth creation proceeding for historical documents that takes into account noisy background and transcription alignment. The proposed ground truth creation is demonstrated for the IAM Historical Handwriting Database (IAM-HistDB) that is currently under construction and will include several hundred Old German manuscripts. With a small set of algorithmic tools and few manual interactions, it is shown how laypersons can efficiently create a ground truth for handwriting recognition.

References

[1]

A. Antonacopoulos and A. Downton (eds.). Special issue on the analysis of historical documents. Int. Journal on Document Analysis and Recognition, 9(2--4):75--77, 2007.

Digital Library

[2]

G. Bal, G. Agam, G. Frieder, and O. Frieder. Interactive degraded document enhancement and ground truth generation. In B. Yanikoglu and K. Berkner, editors, Document Recognition and Retrieval XV, volume 6815 of Proc. SPIE, 2008.

[3]

U. Bhattacharya and B. Chaudhuri. Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals. IEEE Trans. PAMI, 31(3):444--457, 2009.

Digital Library

[4]

S. Feng, N. Howe, and R. Manmatha. A hidden Markov model for alphabet-soup word recognition. In Proc. IEEE Int. Conf. on Frontiers in Handwriting Recognition (ICFHR 2008), pages 210--215, 2008.

[5]

A. Fischer, M. Wüthrich, M. Liwicki, V. Frinken, H. Bunke, G. Viehhauser, and M. Stolz. Automatic transcription of handwritten medieval documents. In Proc. 15th Int. Conf. on Virtual Systems and Multimedia, volume 1, pages 137--142. IEEE, September 2009.

Digital Library

[6]

A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber. A novel connectionist system for improved unconstrained handwriting recognition. IEEE Trans. PAMI, 31(5):855--868, 2009.

Digital Library

[7]

I. Guyon, L. Schomaker, R. Plamondon, M. Liberman, and S. Janet. UNIPEN project of on-line data exchange and recognizer benchmarks. In Proc. 12th Int. Conf. on Pattern Recognition (ICPR), volume 2, pages 29--33, 1994.

[8]

J. J. Hull. A database for handwritten text recognition research. IEEE Trans. PAMI, 16(5):550--554, 1994.

Digital Library

[9]

S. Impedovo, P. Wang, and H. Bunke, editors. Automatic Bankcheck Processing. World Scientific, 1997.

[10]

E. Indermühle, M. Liwicki, and H. Bunke. Combining alignment results for historical handwritten document analysis. In 10th Int. Conf. on Document Analysis and Recognition, pages 1186--1190, 2009.

Digital Library

[11]

L. Likforman-Sulem, A. Zahour, and B. Taconet. Text line segmentation of historical documents: a survey. International Journal on Document Analysis and Recognition (IJDAR), 9(2--4):123--138, 2007.

Digital Library

[12]

M. Liwicki and H. Bunke. IAM-OnDB - an on-line english sentence database acquired from handwritten text on a whiteboard. In Proc. 8th Int. Conf. on Document Analysis and Recognition (ICDAR), volume 2, pages 956--961, 2005.

Digital Library

[13]

M. Liwicki, E. Indermühle, and H. Bunke. Online handwritten text line detection using dynamic programming. In Proc. 9th Int. Conf. on Document Analysis and Recognition, volume 1, pages 447--451, 2007.

Digital Library

[14]

R. Manmatha and J. L. Rothfeder. A scale space approach for automatically segmenting words from historical handwritten documents. IEEE Trans. PAMI, 27(8):1212--1225, 2005.

Digital Library

[15]

U.-V. Marti and H. Bunke. Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. Journal of Pattern Recognition and Art. Intelligence, 15:65--90, 2001.

[16]

U.-V. Marti and H. Bunke. The IAM-database: an English sentence database for off-line handwriting recognition. Int. Journal on Document Analysis and Recognition, 5:39--46, 2002.

[17]

G. Nagy and D. Lopresti. Interactive document processing and digital libraries. In Proc. 2nd Int. Workshop on Document Image Analysis for Libraries (DIAL 2006), pages 2--11. IEEE Computer Society, 2006.

Digital Library

[18]

M. Nakagawa and K. Matsumoto. Collection of on-line handwritten Japanese character pattern databases and their analysis. Int. Journal on Document Analysis and Recognition, 7(1):69--81, 2004.

Digital Library

[19]

K. Ntzios, B. Gatos, I. Pratikakis, T. Konidaris, and S. J. Perantonis. An old Greek handwritten ocr system based on an efficient segmentation-free approach. International Journal on Document Analysis and Recognition (IJDAR), 9(2):179--192, 2007.

Digital Library

[20]

M. Pechwitz, S. Maddouri, V. Maergner, N. Ellouze, and H. Amiri. IFN/ENIT - database of handwritten Arabic words. In Proc. on CIFED, pages 129--136, 2002.

[21]

L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--285, Feb. 1989.

[22]

T. M. Rath and R. Manmatha. Word spotting for historical documents. Int. Journal on Document Analysis and Recognition, 9:139--152, 2007.

Digital Library

[23]

S. Srihari, Y. Shin, and V. Ramanaprasad. A system to read names and addresses on tax forms. Proc. IEEE, 84(7):1038--1049, 1996.

[24]

K. Terasawa and Y. Tanaka. Slit style HOG features for document image word spotting. In 10th Int. Conf. on Document Analysis and Recognition, volume 1, pages 116--120, 2009.

Digital Library

[25]

C. Viard-Gaudin, P. M. Lallican, P. Binter, and S. Knerr. The IRESTE on/off (IRONOFF) dual handwriting database. In Proc. 5th Int. Conf. on Document Analysis and Recognition (ICDAR), pages 455--458, 1999.

Digital Library

[26]

M. Wüthrich, M. Liwicki, A. Fischer, E. Indermühle, H. Bunke, G. Viehhauser, and M. Stolz. Language model integration for the recognition of handwritten medieval documents. In Proc. 10th Int. Conf. on Document Analysis and Recognition, volume 1, pages 211--215. IEEE, July 2009.

Digital Library

[27]

M. Zimmermann and H. Bunke. Automatic segmentation of the IAM off-line database for handwritten English text. In Proc. 16th Int. Conf. on Pattern Recognition, volume 4, pages 35--39, 2002.

Cited By

Chiffoleau F(2024)Keeping It Open: A TEI-based Publication Pipeline for Historical DocumentsJournal of the Text Encoding Initiative10.4000/12s01Issue 15Online publication date: 2024
https://doi.org/10.4000/12s01
AlKendi WGechter FHeyberger LGuyeux C(2024)Advancements and Challenges in Handwritten Text Recognition: A Comprehensive SurveyJournal of Imaging10.3390/jimaging1001001810:1(18)Online publication date: 8-Jan-2024
https://doi.org/10.3390/jimaging10010018
Yang ZZuo SZhou YHe JShi J(2024)A Review of Document Binarization: Main Techniques, New Challenges, and TrendsElectronics10.3390/electronics1307139413:7(1394)Online publication date: 7-Apr-2024
https://doi.org/10.3390/electronics13071394
Show More Cited By

Index Terms

Ground truth creation for handwriting recognition in historical documents
1. Applied computing
  1. Document management and text processing

Recommendations

Handwritten text recognition for historical documents in the transcriptorium project
DATeCH '14: Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage

Transcription of historical handwritten documents is a crucial problem for making easier the access to these documents to the general public. Currently, huge amount of historical handwritten documents are being made available by on-line portals ...
Impact of the ground truth quality for handwriting recognition
SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology

Handwriting recognition is a key technology for accessing the content of old manuscripts, helping to preserve cultural heritage. Deep learning shows an impressive performance in solving this task. However, to achieve its full potential, it requires a ...
BYANJON: A Ground Truth Preparation System for Online Handwritten Bangla Documents
The work reported in this article deals with the ground truth generation scheme for online handwritten Bangla documents at text-line, word, and stroke levels. The aim of the proposed scheme is twofold: firstly, to build a document level database so that ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

June 2010

490 pages

ISBN:9781605587738

DOI:10.1145/1815330

General Chairs:
David Doermann
University of Maryland, College Park
,
Venu Govindaraju
University at Buffalo, SUNY
,
Daniel Lopresti
Lehigh University
,
Prem Natarajan
Raytheon BBN Technologies

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

Swiss National Science Foundation

Conference

DAS '10

DAS '10: The Eighth IAPR International Workshop on Document Analysis Systems

June 9 - 11, 2010

Massachusetts, Boston, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

60
Total Citations
View Citations
855
Total Downloads

Downloads (Last 12 months)39
Downloads (Last 6 weeks)7

Reflects downloads up to 12 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chiffoleau F(2024)Keeping It Open: A TEI-based Publication Pipeline for Historical DocumentsJournal of the Text Encoding Initiative10.4000/12s01Issue 15Online publication date: 2024
https://doi.org/10.4000/12s01
AlKendi WGechter FHeyberger LGuyeux C(2024)Advancements and Challenges in Handwritten Text Recognition: A Comprehensive SurveyJournal of Imaging10.3390/jimaging1001001810:1(18)Online publication date: 8-Jan-2024
https://doi.org/10.3390/jimaging10010018
Yang ZZuo SZhou YHe JShi J(2024)A Review of Document Binarization: Main Techniques, New Challenges, and TrendsElectronics10.3390/electronics1307139413:7(1394)Online publication date: 7-Apr-2024
https://doi.org/10.3390/electronics13071394
Boudraa MBennour AMekhaznia TAlqarafi AMarie RAl-Sarem MDogra A(2024)Revolutionizing Historical Manuscript Analysis: A Deep Learning Approach with Intelligent Feature Extraction for Script ClassificationActa Informatica Pragensia10.18267/j.aip.23913:2(251-272)Online publication date: 4-Aug-2024
https://doi.org/10.18267/j.aip.239
Zottin SDe Nardin AColombi EPiciarelli CPavan FForesti G(2024)U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscriptsNeural Computing and Applications10.1007/s00521-023-09356-536:20(11777-11789)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-023-09356-5
Neto ABezerra BAraújo SSouza WAlves KOliveira MLins SHazin HRocha PToselli A(2024)BRESSAY: A Brazilian Portuguese Dataset for Offline Handwritten Text RecognitionDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70536-6_19(315-333)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70536-6_19
Parres DAnitei DParedes RSánchez JBenedí J(2024)Speed-Up Pre-trained Vision Encoder–Decoder Transformers by Leveraging Lightweight Mixer Layers for Text RecognitionDocument Analysis Systems10.1007/978-3-031-70442-0_17(277-294)Online publication date: 11-Sep-2024
https://doi.org/10.1007/978-3-031-70442-0_17
Pinche A(2023)Generic HTR Models for Medieval Manuscripts. The CREMMALab ProjectJournal of Data Mining & Digital Humanities10.46298/jdmdh.10252Historical Documents and...Online publication date: 16-Oct-2023
https://doi.org/10.46298/jdmdh.10252
Parres DParedes R(2023)Fine-Tuning Vision Encoder–Decoder Transformers for Handwriting Text Recognition on Historical DocumentsDocument Analysis and Recognition - ICDAR 202310.1007/978-3-031-41685-9_16(253-268)Online publication date: 19-Aug-2023
https://doi.org/10.1007/978-3-031-41685-9_16
Pach J(2023)Database of Fragments of Medieval Codices of the 11th–12th Centuries – The Uniqueness of Requirements and DataComputational Science – ICCS 202310.1007/978-3-031-36027-5_8(104-112)Online publication date: 26-Jun-2023
https://doi.org/10.1007/978-3-031-36027-5_8
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten