Qoʻlyozma matn tasvirini gibrid model yordamida tanib olish
Аннотация
В распознавании узбекских рукописных букв важным вопросом является сегментация и ее распознавание.Кривизна рукописного текста,проблемы, связанные со скорописью и касанием слов друг-другу, неправильный расположении диакритических знаков и наличием подъемов и спадов приводят к обработке проблемы сегментации. В рукописях эффективность линейной сегментации и сегментации на слова показана на основе результатов, полученных от программного обеспечения на основе модели нейронной сети CNN+LSTM+CTC.
Oʻzbek matni qoʻlyozma harflarni tanib olishda segmentatsiya masalasi va uni tanib olish muhim hisoblanadi. Qo’lyozma matnning egriligi, bir-birini takrorlashi va tegishi bilan bog’liq muammolar, kursiv bog’lanish, noto’g’ri pozitsiya diakritik belgilar, ko’tarilish va tushish borligi segmentatsiya masalasini qayta ishlashga olib keladi. Qo’lyozmalarda CNN+LSTM+CTC neyron tarmoq modeli asosida dasturiy ta’minotdan olingan natija asosida qator segmentatsiyasi va soʻzlarga ajratish samaradorligi koʻrsatib berilgan.
In the recognition of Uzbek handwritten letters, an important issue is segmentation and its recognition. The curvature of the handwriting, cursive and word-toword problems, incorrect placement of diacritics, and the presence of ups and downs lead to the processing of the segmentation problem. In the manuscripts, the performance of linear and word segmentation is shown based on results obtained from software based on the CNN+LSTM+CTC neural network model.
Oʻzbek matni qoʻlyozma harflarni tanib olishda segmentatsiya masalasi va uni tanib olish muhim hisoblanadi. Qo’lyozma matnning egriligi, bir-birini takrorlashi va tegishi bilan bog’liq muammolar, kursiv bog’lanish, noto’g’ri pozitsiya diakritik belgilar, ko’tarilish va tushish borligi segmentatsiya masalasini qayta ishlashga olib keladi. Qo’lyozmalarda CNN+LSTM+CTC neyron tarmoq modeli asosida dasturiy ta’minotdan olingan natija asosida qator segmentatsiyasi va soʻzlarga ajratish samaradorligi koʻrsatib berilgan.
In the recognition of Uzbek handwritten letters, an important issue is segmentation and its recognition. The curvature of the handwriting, cursive and word-toword problems, incorrect placement of diacritics, and the presence of ups and downs lead to the processing of the segmentation problem. In the manuscripts, the performance of linear and word segmentation is shown based on results obtained from software based on the CNN+LSTM+CTC neural network model.