2014/12/15 by Gundram Leifert, Leifert, Gundram, Roger Labahn +3 · 1 citation
Computer Science · #68T05 #68T10 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Natural Language Processing Techniques #Neural and Evolutionary Computing (cs.NE) #Topic Modeling #cs.CV #cs.NE #msc:68T05 #msc:68T10
paper · pdf · doi:10.48550/arxiv.1412.6061
http://www.nist.gov/itl/iad/mig/upload/OpenHaRT2013_SysDesc_CITLAB.pdf
arxiv created 2014/12/15 · openalex publication_date 2014/12/15 · arxiv updated 2014/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the recent years it turned out that multidimensional recurrent neural networks (MDRNN) perform very well for offline handwriting recognition tasks like the OpenHaRT 2013 evaluation DIR. With suitable writing preprocessing and dictionary lookup, our ARGUS software completed this task with an error rate of 26.27% in its primary setup.