A bilingual text detection in natural images using heuristic and unsupervised learning | ||
| Journal of AI and Data Mining | ||
| دوره 10، شماره 4، بهمن 2022، صفحه 449-466 اصل مقاله (2.14 M) | ||
| نوع مقاله: Methodologies | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2022.11089.2260 | ||
| نویسندگان | ||
| S. Bayatpour؛ M. Sharghi* | ||
| Faculty of Engineering and Technology, Alzahra University, Tehran, Iran. | ||
| چکیده | ||
| Digital images are being produced in a massive number every day. A component that may exist in digital images is text. Textual information can be extracted and used in a variety of fields. Noise, blur, distortions, occlusion, font variation, alignments, and orientation, are among the main challenges for text detection in natural images. Despite many advances in text detection algorithms, there is not yet a single algorithm that addresses all of the above problems successfully. Furthermore, most of the proposed algorithms can only detect horizontal texts and a very small fraction of them consider Farsi language. In this paper, a method is proposed for detecting multi-orientated texts in both Farsi and English languages. We have defined seven geometric features to distinguish text components from the background and proposed a new contrast enhancement method for text detection algorithms. Our experimental results indicate that the proposed method achieves a high performance in text detection on natural images. | ||
| کلیدواژهها | ||
| Text Detection؛ Natural Images؛ Mean Shift Clustering؛ bilingual Text | ||
| مراجع | ||
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