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Ray et al., 2015 - Google Patents

A hypothesize-and-verify framework for text recognition using deep recurrent neural networks

Ray et al., 2015

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Document ID
131558989154715432
Author
Ray A
Rajeswar S
Chaudhury S
Publication year
Publication venue
2015 13th International Conference on Document Analysis and Recognition (ICDAR)

External Links

Snippet

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
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    • G06K9/6807—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
    • G06K9/6842—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German
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