The IAM-database: An English sentence database for offline handwriting recognition. In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo/Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words

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  1. Morera, Ángel; Sánchez, Ángel; Vélez, José Francisco; Moreno, Ana Belén: Gender and handedness prediction from offline handwriting using convolutional neural networks (2018)
  2. Helli, Behzad; Moghaddam, Mohsen Ebrahimi: An off-line cheque handwritten forgery detection based on feature route density matrix (2014) ioport
  3. Mahmoud, Sabri A.; Ahmad, Irfan; Al-Khatib, Wasfi G.; Alshayeb, Mohammad; Tanvir Parvez, Mohammad; Märgner, Volker; Fink, Gernot A.: KHATT: an open Arabic offline handwritten text database (2014) ioport
  4. Su, Tonghua: Chinese handwriting recognition. An algorithmic perspective (2013)
  5. Liang, Y.; Fairhurst, M. C.; Guest, R. M.: A synthesised word approach to word retrieval in handwritten documents (2012) ioport
  6. Siddiqi, Imran; Vincent, Nicole: Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features (2010)
  7. Toselli, Alejandro H.; Romero, Verónica; Pastor, Moisés; Vidal, Enrique: Multimodal interactive transcription of text images (2010)
  8. Plötz, Thomas; Fink, Gernot A.: Markov models for offline handwriting recognition: a survey (2009) ioport
  9. Zhu, Guangyu; Yu, Xiaodong; Li, Yi; Doermann, David: Language identification for handwritten document images using a shape codebook (2009)
  10. Bertolami, Roman; Bunke, Horst: Hidden Markov model-based ensemble methods for offline handwritten text line recognition (2008)
  11. Wienecke, Markus; Fink, Gernot A.; Sagerer, Gerhard: Toward automatic video-based whiteboard reading (2005) ioport
  12. Günter, Simon; Bunke, Horst: New boosting algorithms for classification problems with large number of classes applied to a handwritten word recognition task (2003)
  13. Marti, U.-V.; Bunke, H.: The IAM-database: An English sentence database for offline handwriting recognition (2002)