Camera-Based Document Analysis and Recognition: 5th by Masakazu Iwamura, Faisal Shafait

By Masakazu Iwamura, Faisal Shafait

This e-book constitutes the completely refereed post-workshop court cases 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 offered have been rigorously chosen in the course of rounds of reviewing and development from quite a few unique submissions. meant to provide a picture of the state of the art examine within the box of digicam dependent record research and popularity, the papers are geared up in topical sections on textual content detection and popularity in scene photographs and camera-based systems.

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Extra resources for Camera-Based Document Analysis and Recognition: 5th International Workshop, CBDAR 2013, Washington, DC, USA, August 23, 2013, Revised Selected Papers

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The above is the main factor behind the robustness of the proposed approach for extraction of texts from general camera captured images. Rest of this article is organized as follows. Section 2 provides the background of the present study. Section 3 describes the proposed approach in some details while the postprocessing operations are described in Sect. 4. Section 5 presents extensive simulation results using the sample scene images of ICDAR 2003 Robust Reading Competitions Database [7]. Finally, Sect.

1(b), (b) final output of the proposed method for binarization of specular region corresponding to the sample image of Fig. 1(b). labeled as diffuse. However, in our case, choosing a very high value of τ enables us to identify only those pixels which are highly specular, that is information contained in those pixels can be assumed to be lost under the high flash of light. Hence unlike [9], we don’t need to enhance the specular pixels as it doesn’t contain any information. This assumption has the drawback that very bright (almost white) text pixels get identified as specular pixels.

It is observed in experiments that the speed mainly depends on the MSER parameter Δ that represents the range of intensities where the regions are stable [19]. 45 images/second. When setting Δ = 6 the number of components reduces 56 Q. Ye and D. Doermann Table 1. Performance (%) comparison of text detection different color channels. 36 Table 2. Performance (%) comparison of text detection approaches on ICDAR2011 robust reading competition dataset. 63 Fig. 6. Performance under different component classifier numbers.

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