Camera-Based Document Analysis and Recognition: 5th by Masakazu Iwamura, Faisal Shafait
By Masakazu Iwamura, Faisal Shafait
This ebook constitutes the completely refereed post-workshop complaints of the fifth overseas Workshop on Camera-Based record research and popularity, CBDAR 2013, held in Washington, DC, united states, in August 2013. The 14 revised complete papers awarded have been rigorously chosen in the course of rounds of reviewing and development from various unique submissions. meant to offer a picture of the state of the art examine 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 photographs and camera-based systems.
Read or Download Camera-Based Document Analysis and Recognition: 5th International Workshop, CBDAR 2013, Washington, DC, USA, August 23, 2013, Revised Selected Papers PDF
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Additional info for Camera-Based Document Analysis and Recognition: 5th International Workshop, CBDAR 2013, Washington, DC, USA, August 23, 2013, Revised Selected Papers
Performance (%) comparison of text detection approaches on ICDAR2011 robust reading competition dataset. 63 Fig. 6. Performance under diﬀerent component classiﬁer numbers. 1 %. Figure 8 shows some detection examples, where most of the text objects are correctly detected with few false positives. The text objects are in complex background and can have low resolution or low contrast. This shows that the proposed approach can correctly capture text patterns of large variations simultaneously with an integrated discrimination.
6. In Fig. 6(a), ‘1’ represents foreground pixels and ‘0’ represents background pixels. Each object pixel of this binary image is assigned a value which is its distance from the nearest background pixel as shown in Fig. 6(b). At each non-zero pixel of D∗ , we consider a 3 × 3 window to obtain the local maximum at that pixel. If this pixel value equals the local maximum, we store the pixel value in a list < T > for further processing. In fact, such a pixel value (a local maximum value) is an estimate of half of the local stroke thickness.
The MSER algorithm adaptively detects stable color regions and provides a good solution to localize the components. In [8,10], MSERs from H, S, I and gradient channels are integrated to detect components. An exhaustive search is then applied to group components into regions and a text level classiﬁer is used for classiﬁcation of these regions. In , Koo et al present a text detection approach based on MSERs and two classiﬁers. The ﬁrst classiﬁer is trained on AdaBoost that determines the adjacency relationship and cluster components by using pairwise relations.