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低數(shù)據(jù)資源條件下基于結(jié)構(gòu)信息共享的無切分維文文檔識(shí)別字符建模

姜志威 丁曉青 彭良瑞 劉長松

姜志威, 丁曉青, 彭良瑞, 劉長松. 低數(shù)據(jù)資源條件下基于結(jié)構(gòu)信息共享的無切分維文文檔識(shí)別字符建模[J]. 電子與信息學(xué)報(bào), 2015, 37(9): 2103-2109. doi: 10.11999/JEIT150019
引用本文: 姜志威, 丁曉青, 彭良瑞, 劉長松. 低數(shù)據(jù)資源條件下基于結(jié)構(gòu)信息共享的無切分維文文檔識(shí)別字符建模[J]. 電子與信息學(xué)報(bào), 2015, 37(9): 2103-2109. doi: 10.11999/JEIT150019
Jiang Zhi-wei, Ding Xiao-qing, Peng Liang-rui, Liu Chang-song. Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2103-2109. doi: 10.11999/JEIT150019
Citation: Jiang Zhi-wei, Ding Xiao-qing, Peng Liang-rui, Liu Chang-song. Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2103-2109. doi: 10.11999/JEIT150019

低數(shù)據(jù)資源條件下基于結(jié)構(gòu)信息共享的無切分維文文檔識(shí)別字符建模

doi: 10.11999/JEIT150019
基金項(xiàng)目: 

國家自然科學(xué)基金(61032008)和國家973計(jì)劃項(xiàng)目(2013CB329403)

Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions

  • 摘要: 無切分維吾爾文文檔識(shí)別技術(shù)能夠有效避免字符切分錯(cuò)誤,但是對(duì)于低數(shù)據(jù)資源的新樣本類型,原有模型往往難以獲得較高的識(shí)別性能。為此,該文提出共享常用維文字體間相對(duì)穩(wěn)定的字符結(jié)構(gòu)信息,并用Bootstrap方法提高樣本利用效率的解決方法。通過在實(shí)際書籍樣本上的實(shí)驗(yàn)表明,僅利用規(guī)模約原始訓(xùn)練樣本1/5的新類型樣本,該方法在測試集上的平均字符識(shí)別準(zhǔn)確率就可以達(dá)到95.05%;而與常用的最大后驗(yàn)概率估計(jì)方法相比,也能使識(shí)別錯(cuò)誤率相對(duì)降低55.76%~63.84%。因此,該方法能夠有效解決低數(shù)據(jù)資源條件下的維文字符建模問題,實(shí)現(xiàn)對(duì)新樣本類型的高性能識(shí)別。
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出版歷程
  • 收稿日期:  2015-01-06
  • 修回日期:  2016-03-25
  • 刊出日期:  2015-09-19

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