Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1410006(2021)
Layout Segmentation and Description of Tibetan Document Images Based on Adaptive Run Length Smoothing Algorithm
Fig. 4. ARLSA process of text lines in Tibetan document images. (a) Binary figures; (b) ARLSA processing results
Fig. 5. Filtering results in connected domains. (a) ARLSA processing result; (b) rectangular outer box for connected domains; (c) filtering result
Fig. 6. Vowel attribution separated from baseline. (a) ARLSA processing result of text line; (b) centroids of connected components; (c) vertical distance between centroids; (d) filtering result
Fig. 7. Cluster analysis graphs of random segmentation block sample data. (a) Random sample data distribution; (b) K=3 cluster
Fig. 9. Word segmentation and recognition. (a) Separation of vowels and base words, and word adhesion; (b) segmentation result; (c) recognition result
Fig. 10. Layout analysis results. (a) Original image; (b) target connected region; (c) classification result of layout elements; (d) layout description
Fig. 11. Wrong classification images. (a)(c) Original images; (b)(d) wrong classification results
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Yuanyuan Chen, Weilan Wang, Huaming Liu, Zhengqi Cai, Penghai Zhao. Layout Segmentation and Description of Tibetan Document Images Based on Adaptive Run Length Smoothing Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410006
Category: Image Processing
Received: Sep. 21, 2020
Accepted: Nov. 12, 2020
Published Online: Jul. 14, 2021
The Author Email: Weilan Wang (wangweilan@xbmu.edu.cn)