کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532028 869898 2015 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Tensor representation learning based image patch analysis for text identification and recognition
ترجمه فارسی عنوان
تجزیه و تحلیل تنسور مبتنی بر یادگیری مبتنی بر تصحیح پچ برای شناسایی متن و شناخت
کلمات کلیدی
یادگیری نمایندگی تانسور، همگرایی، درک سند باستان، شناسه متن، تشخیص متن
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• A novel model, tensor representation learning based image patch analysis (TRL-IPA), is proposed for document understanding.
• TRL-IPA is built on a general formulation of the convergent tensor representation learning (CTRL) algorithms.
• The CTRL algorithms are theoretically guaranteed to converge to a local optimal solution of the learning problem.
• Extensive experiments demonstrate the superiority of TRL-IPA over related vector and tensor representation based approaches.

In this paper, we introduce a novel framework for text identification and recognition, called tensor representation learning based image patch analysis (TRL-IPA). Unlike most of previous text identification approaches, which can only be applied to binarized images, TRL-IPA can be directly applied to gray level and color images. TRL-IPA is built on a general formulation of the convergent tensor representation learning (CTRL) algorithms. In the implementation of TRL-IPA, image patches are represented in the form of tensors, while low dimensional representations of these tensors are learned via a CTRL algorithm. To identify text regions in new coming document images, a random forest classifier is trained in the learned tensor subspace. Moreover, the TRL-IPA framework can be straightforwardly applied to recognition problems, such as handwritten digits recognition. We conducted extensive experiments on ancient Chinese, Arabic and Cyrillic document images, to evaluate TRL-IPA on text identification tasks. Experimental results demonstrate its effectiveness and robustness. In addition, recognition results on images of handwritten digits show its advantage over state-of-the-art vector and tensor representation based approaches.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 48, Issue 4, April 2015, Pages 1211–1224
نویسندگان
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