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

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)

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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

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

摘要

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。
- 文字識(shí)別 /
- 隱馬爾可夫模型 /
- 統(tǒng)計(jì)學(xué)習(xí) /
- 維吾爾文
Abstract: Although segmentation-free Uyghur character document recognition can efficiently avoid character segmentation error, it does not work well on low-resource new-type samples. This paper suggests sharing stable character structure among different Uyghur fonts, and improves the efficiency of utilizing samples through Bootstrap. Experiments are made on new-type book samples, which contains only 1/5 training sample amount than the original. The average character recognition accuracy of the proposed method on test samples is 95.05%, and has 55.76%~63.84% recognition error rate relative decrease than the one of Maximum A Posteriori (MAP) method. Therefore, the proposed method can accomplish accurate Uyghur character model training under low data resource conditions.
- Character recognition /
- Hidden Markov Model (HMM) /
- Statistical learning /
- Uyghur character

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