By Thotreingam Kasar, Angarai G. Ramakrishnan (auth.), Masakazu Iwamura, Faisal Shafait (eds.)
This e-book constitutes the completely refereed post-workshop-proceedings of the 4th foreign Workshop on Camera-Based rfile research and popularity, CBDAR 2011, held in Beijing, China, in September 2011. The thirteen revised complete papers provided have been rigorously chosen in the course of a moment around of reviewing and development from quite a few unique submissions. meant to provide a image of the state of the art learn within the box of digicam dependent record research and popularity, the papers are equipped in topical sections on textual content detection and popularity in scene pictures, camera-based platforms, and datasets and evaluation.
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Extra info for Camera-Based Document Analysis and Recognition: 4th International Workshop, CBDAR 2011, Beijing, China, September 22, 2011, Revised Selected Papers
In fact, the corresponding pixels form a skeleton like representation of the object as shown in Fig. 8. Such a D value (a local maximum value) is an estimate of half of the local stroke thickness. (a) (b) Fig. 8. (a) A sub-image of the image shown in Fig. 5(a); (b) skeleton-like representation of the object in (a) formed by the pixels in its < T > Finally, we compute the mean (μ) and the standard deviation (σ) of the local stroke thickness values stored in < T >. If μ > 2σ. (well-known 2-σ limit used in statistical process control), we decide that the thickness of the underlying stroke is nearly uniform and select the sub-image S as a candidate text region.
10–16 (2005) 13. : Robust wide baseline stereo from maximally stable extremal regions. In: BMVC 2002 (2002) 14. : Wearable visual robots. Personal and Ubiquitous Computing 6, 37–48 (2002) A Head-Mounted Device for Recognizing Text in Natural Scenes 41 15. : A framework towards realtime detection and tracking of text. In: CBDAR 2007, pp. 10–17 (2007) 16. : A robust method for tracking scene text in video imagery. In: CBDAR 2005 (2005) 17. : A Method for Text Localization and Recognition in RealWorld Images.
First, MSER+ regions are obtained by applying the MSER algorithm on the image. This produces light regions inside dark ones. Then MSER- regions are obtained by applying the MSER algorithm to the inverse (negative) of the original image which produces dark regions inside light ones. The sets of regions returned by each pass are disjoint and both passes are needed to detect light text on dark backgrounds and dark text on light backgrounds. The algorithm can be easily modiﬁed to return a hierarchical MSER tree; an example output can be seen in Fig.
Camera-Based Document Analysis and Recognition: 4th International Workshop, CBDAR 2011, Beijing, China, September 22, 2011, Revised Selected Papers by Thotreingam Kasar, Angarai G. Ramakrishnan (auth.), Masakazu Iwamura, Faisal Shafait (eds.)