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handwriting [2017/09/21 15:39] (current)
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 +http://​people.idsia.ch/​~juergen/​handwriting.html
  
 +https://​tel.archives-ouvertes.fr/​tel-01249405/​document ​ Deep Neural Networks for Large Vocabulary
 +Handwritten Text Recognition
 +
 +http://​pkmital.com/​home/​2015/​02/​06/​handwriting-recognition-with-lstms-and-ofxcaffe/  ​
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 +http://​human.ait.kyushu-u.ac.jp/​publications/​ICDAR2015-Frinken.pdf
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 +http://​www.rsipvision.com/​deep-learning-for-ocr/​
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 +Jaderberg, Max, Andrea Vedaldi, and Andrew Zisserman. “Deep features for text spotting.” Computer Vision–ECCV 2014. Springer International Publishing, 2014. 512-528.
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 +Cireşan, Dan C., et al. “Handwritten digit recognition with a committee of deep neural nets on gpus.” arXiv preprint arXiv:​1103.4487 (2011).
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 +Jaderberg, Max, et al. “Synthetic data and artificial neural networks for natural scene text recognition.” arXiv preprint arXiv:​1406.2227 (2014).
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 +Cireşan, Dan C., Ueli Meier, and Jürgen Schmidhuber. “Transfer learning for Latin and Chinese characters with deep neural networks.” Neural Networks (IJCNN), The 2012 International Joint Conference on. IEEE, 2012.
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 +Achanta, Rakesh, and Trevor Hastie. “Telugu OCR Framework using Deep Learning.” arXiv preprint arXiv:​1509.05962 (2015).
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 + 
 +http://​www.fki.inf.unibe.ch/​databases/​iam-on-line-handwriting-database
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 +http://​distill.pub/​2016/​handwriting/​
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 +https://​github.com/​hardmaru/​write-rnn-tensorflow
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 +https://​github.com/​codeaudit/​sketch-rnn
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 +http://​www.cedar.buffalo.edu/​handwriting/​HRdatabase.html
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 +https://​srdata.nist.gov/​gateway/​gateway?​keyword=handwriting+recognition
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 +http://​www.gavo.t.u-tokyo.ac.jp/​~qiao/​database.html
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 +https://​github.com/​jan-auer/​rnnlib-iam
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 +http://​www.ee.surrey.ac.uk/​CVSSP/​demos/​chars74k/​
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 +http://​www.openocr.net/​
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 +http://​sdaps.org/​
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 +https://​quexf.acspri.org.au/​
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 +https://​arxiv.org/​pdf/​1709.06389v1.pdf A General Framework for the Recognition of Online Handwritten Graphics
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 +We model a graphic as a labeled graph generated from a graph grammar. Non-terminal vertices represent subcomponents,​ terminal vertices represent symbols, and edges represent relations between subcomponents or symbols. We then model the recognition problem as a graph parsing problem: given an input stroke set, we search for a parse tree that represents the best interpretation of the input. Our graph parsing algorithm generates multiple interpretations (consistent with the grammar) and then we extract an optimal interpretation according to a cost function that takes into consideration the likelihood scores of symbols and structures. The parsing algorithm consists in recursively partitioning the stroke set according to structures defined in the grammar and it does not impose constraints present in some previous works (e.g. stroke ordering). ​