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dc.contributor.authorEisa, Abeer
dc.contributor.authorAbdalla, Lina
dc.contributor.authorAhmed, Mohanad
dc.date.accessioned2020-05-06T08:37:36Z
dc.date.available2020-05-06T08:37:36Z
dc.date.issued2019
dc.identifier.citationEisa, A., Abdalla, L., & Ahmed, M. (2019). Online Handwriting Recognition Using Encoder-Decoder Model. 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). doi:10.1109/iccceee46830.2019.9071037
dc.identifier.isbn978-1-7281-1007-3
dc.identifier.doi10.1109/ICCCEEE46830.2019.9071037
dc.identifier.urihttp://hdl.handle.net/10754/662741
dc.description.abstractIn this paper we propose a system that is capable of recognizing raw online handwritten data. The system consists of an advanced type of neural network known as LSTM (Long Short Term Memory) Encoder-decoder combined with a customized attention mechanism layer. The attention mechanism has greatly enhanced the system performance from a low character level accuracy of 53% to an excellent accuracy of 96%. Moreover, the system involves a segmentation algorithm designed to divide the sentences into segments of lines. For the training and testing we employ the IAM On-Line Handwriting database, the source can be found here [1]. The accuracy can be improved even further by integrating our system with a language model to spell check the outputs.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9071037/
dc.relation.urlhttps://ieeexplore.ieee.org/document/9071037/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9071037
dc.rightsArchived with thanks to IEEE
dc.subjectonline handwriting
dc.subjectneural network
dc.subjectLSTM
dc.subjectsequence to sequence
dc.subjectattention
dc.subjectencoder-decoder
dc.titleOnline Handwriting Recognition Using Encoder-Decoder Model
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.date21-23 Sept. 2019
dc.conference.name2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)
dc.conference.locationKhartoum, Sudan
dc.eprint.versionPre-print
dc.contributor.institutionUniversity of Khartoum,Faculty of Engineering,Khartoum,Sudan,321
kaust.personAhmed, Mohanad


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