unified detection and recognition for reading text in scene images
unified detection and recognition for reading text in scene images
unified detection and recognition for reading text in scene images
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Figure 1.5. Un<strong>in</strong><strong>for</strong>med segmentation can make <strong>recognition</strong> difficult. Top: Input<br />
image. Left: Probability of <strong>for</strong>eground under a Gaussian mixture model (k = 3, HSV<br />
color space). Right: B<strong>in</strong>arization by threshold (p (<strong>for</strong>eground|k = 3, image) ≥ 1 2 ).<br />
One other closely related work is that of Tu et al. [118]. Here, discrim<strong>in</strong>ative<br />
models are used as bottom-up proposal distributions <strong>in</strong> a large generative model.<br />
Discrim<strong>in</strong>ative <strong>text</strong> detectors are used, <strong>and</strong> then a generative model must be able to<br />
expla<strong>in</strong> the pixel data as a contour describ<strong>in</strong>g a known character. They claim the<br />
advantage of the generative model is that it ensures consistent regions. However,<br />
spatially coupled discrim<strong>in</strong>ative models can overcome this [61, 124]. While their<br />
bottom-up detectors seem to do a reasonable job of detect<strong>in</strong>g <strong>text</strong> to subsequently be<br />
expla<strong>in</strong>ed, the generative models <strong>for</strong> characters are <strong>in</strong>dependent of one another. In<br />
other words, there is no coupl<strong>in</strong>g of <strong>in</strong><strong>for</strong>mation based on where a character should<br />
appear (i.e., next to other characters), nor how it should appear (similar to nearby<br />
characters). The models <strong>in</strong> this thesis will demonstrate the utility of such <strong>in</strong><strong>for</strong>mation<br />
<strong>for</strong> <strong>in</strong>terpret<strong>in</strong>g <strong>scene</strong> <strong>text</strong>.<br />
We can broadly categorize the systems <strong>for</strong> robust character <strong>recognition</strong> along the<br />
follow<strong>in</strong>g dimensions:<br />
• B<strong>in</strong>arization [87, 80, 113] versus grayscale feature extraction [23, 62, 52, 118]<br />
• Separate <strong>detection</strong>/segmentation [80, 113, 23] versus <strong>in</strong>tegrated <strong>detection</strong>/segmentation<br />
<strong>and</strong> <strong>recognition</strong> [87, 62, 52, 118]<br />
• Language <strong>in</strong>dependent [87, 23, 62, 118] versus language post-process<strong>in</strong>g [80, 113]<br />
versus <strong>in</strong>tegrated language models [52].<br />
Although some OCR systems have a fully <strong>in</strong>tegrated language model [5, 14, 52], the<br />
systems thus far designed <strong>for</strong> <strong>read<strong>in</strong>g</strong> <strong>scene</strong> <strong>text</strong> either use no language <strong>in</strong><strong>for</strong>mation<br />
[87, 22, 62] or, at best, post-process<strong>in</strong>g [80, 113]. Moreover, as the most difficult<br />
<strong>scene</strong> <strong>text</strong> suffers from the difficulties listed <strong>in</strong> Table 1.1 on page 6, b<strong>in</strong>arization <strong>and</strong><br />
segmentation will become <strong>in</strong>creas<strong>in</strong>gly difficult without regard <strong>for</strong> <strong>in</strong>terpretation (see<br />
Figure 1.5). The effectiveness of <strong>in</strong>tegrat<strong>in</strong>g language with simultaneous segmentation<br />
<strong>and</strong> <strong>recognition</strong> on grayscale—not b<strong>in</strong>ary—<strong>images</strong> was demonstrated <strong>in</strong> the camerabased<br />
OCR system of Jacobs et al. [52]. Research on noisy <strong>and</strong> degraded documents<br />
has recognized this fact <strong>for</strong> some time. Indeed, this may expla<strong>in</strong> some of the success<br />
of <strong>text</strong> <strong>detection</strong> <strong>and</strong> <strong>recognition</strong> systems that use commercial OCR systems (e.g.,<br />
[132, 131, 21]).<br />
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