Entry Sundaram:2013:AFB from talip.bib

Last update: Sun Oct 15 02:55:04 MDT 2017                Valid HTML 3.2!

Index sections

Top | Symbols | Numbers | Math | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

BibTeX entry

@Article{Sundaram:2013:AFB,
  author =       "Suresh Sundaram and A. G. Ramakrishnan",
  title =        "Attention-Feedback Based Robust Segmentation of Online
                 Handwritten Isolated {Tamil} Words",
  journal =      j-TALIP,
  volume =       "12",
  number =       "1",
  pages =        "4:1--4:??",
  month =        mar,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2425327.2425331",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Sat Mar 2 09:25:42 MST 2013",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "In this article, we propose a lexicon-free,
                 script-dependent approach to segment online handwritten
                 isolated Tamil words into its constituent symbols. Our
                 proposed segmentation strategy comprises two modules,
                 namely the (1) Dominant Overlap Criterion Segmentation
                 (DOCS) module and (2) Attention Feedback Segmentation
                 (AFS) module. Based on a bounding box overlap criterion
                 in the DOCS module, the input word is first segmented
                 into stroke groups. A stroke group may at times
                 correspond to a part of a valid symbol
                 (over-segmentation) or a merger of valid symbols
                 (under-segmentation). Attention on specific features in
                 the AFS module serve in detecting possibly
                 over-segmented or under-segmented stroke groups.
                 Thereafter, feedbacks from the SVM classifier
                 likelihoods and stroke-group based features are
                 considered in modifying the suspected stroke groups to
                 form valid symbols. The proposed scheme is tested on a
                 set of 10000 isolated handwritten words (containing
                 53,246 Tamil symbols). The results show that the DOCS
                 module achieves a symbol-level segmentation accuracy of
                 98.1\%, which improves to as high as 99.7\% after the
                 AFS strategy. This in turn entails a symbol recognition
                 rate of 83.9\% (at the DOCS module) and 88.4\% (after
                 the AFS module). The resulting word recognition rates
                 at the DOCS and AFS modules are found to be, 50.9\% and
                 64.9\% respectively, without any postprocessing.",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?&idx=J820",
}

Related entries