کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
525552 868978 2016 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Localizing scene texts by fuzzy inference systems and low rank matrix recovery model
ترجمه فارسی عنوان
محلی سازی متن صحنه با استفاده از سیستم استنتاج فازی و مدل بازیابی ماتریس پایین رتبه
کلمات کلیدی
بازیابی ماتریس پایین رتبه محلی سازی متن صحنه، حداکثر پایدار مناطق افراطی، سیستم استنتاج فازی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• Focused and incidental scene text images are processed in a separate manner.
• Low rank matrix recovery is exploited to process the incidental scene text images.
• A text confidence map was designed via fuzzy inference system.
• The proposed algorithm handles both Latin and Farsi/Arabic scripts.
• Farsi/Arabic scene texts at arbitrary orientations are localized for the first time.

In this paper a framework is proposed to localize both Farsi/Arabic and Latin scene texts with different sizes, fonts and orientations. First, candidate text regions are extracted via an MSER detector enhanced by weighted median filtering to adopt the low resolution texts. At the same time based on fuzzy inference system (FIS), the input image is classified into images with a focused text content and incidental scene text images which the image does not focus on the text content. For the focused scene text images the non-text candidates are filtered via an FIS. On the other hand, for the incidental scene text images apart from the FIS, an extra filtering algorithm based on low rank matrix recovery is proposed. Finally, a new approach based on the clustering, minimum area rectangle and radon transform techniques is proposed to create the single arbitrarily oriented text lines from the remaining text regions. To evaluate the proposed algorithm, we created a collection of natural images containing both Farsi/Arabic and Latin texts. Compared with the state-of-the-art methods, the proposed method achieves the best performance on our and Epshtein datasets and competitive performances on the ICDAR dataset.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 142, January 2016, Pages 94–110
نویسندگان
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