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 1.1. Figure from the patent <strong>for</strong> the first practical OCR system [105].<br />
font consistency, l<strong>in</strong>guistic properties, <strong>and</strong> a lexicon (Chapter 4). This <strong>unified</strong> <strong>read<strong>in</strong>g</strong><br />
model outper<strong>for</strong>ms approaches us<strong>in</strong>g similar <strong>in</strong><strong>for</strong>mation <strong>in</strong> the more traditional<br />
pipel<strong>in</strong>ed architecture.<br />
Detection <strong>and</strong> <strong>recognition</strong> are not processes that happen <strong>in</strong> isolation, but that <strong>in</strong>stead<br />
require feedback <strong>and</strong> communication <strong>for</strong> optimal per<strong>for</strong>mance. Toward this end,<br />
we <strong>in</strong>troduce a template-based local feature model that can be utilized <strong>for</strong> <strong>detection</strong><br />
or <strong>recognition</strong> (Chapter 5). We achieve better results with fewer features by select<strong>in</strong>g<br />
features <strong>and</strong> tra<strong>in</strong><strong>in</strong>g the model on the jo<strong>in</strong>t task of <strong>detection</strong> <strong>and</strong> <strong>recognition</strong>,<br />
rather than <strong>for</strong> each sub-task <strong>in</strong>dependently. F<strong>in</strong>ally, we unify character <strong>and</strong> word<br />
segmentation with <strong>recognition</strong>, <strong>in</strong>tegrat<strong>in</strong>g these processes <strong>in</strong> a s<strong>in</strong>gle model that can<br />
evaluate <strong>and</strong> compare <strong>in</strong>terpretations with maximal awareness (Chapter 6).<br />
Next we describe the problem of <strong>scene</strong> <strong>text</strong> <strong>read<strong>in</strong>g</strong> <strong>and</strong> contrast it with the probem<br />
of document process<strong>in</strong>g. In the rema<strong>in</strong>der of this chapter we review some of the broad<br />
literature relevant to the tasks of document process<strong>in</strong>g, <strong>text</strong> <strong>detection</strong>, <strong>and</strong> character<br />
<strong>recognition</strong>. Chapter 2 will then review the common, underly<strong>in</strong>g framework <strong>for</strong> the<br />
probability models used <strong>in</strong> the rest of the thesis.<br />
1.1 Scene Text<br />
There is a long history of research <strong>and</strong> development of automatic readers. The first<br />
practical system was a patent filed <strong>in</strong> 1951 by David H. Shepard [105]. This device<br />
(see Figure 1.1) converted typewritten documents <strong>in</strong>to punch cards <strong>in</strong> a magaz<strong>in</strong>e<br />
subscription department. Of course, hardware devices <strong>and</strong> software techniques have<br />
improved a great deal <strong>in</strong> the fifty <strong>in</strong>terven<strong>in</strong>g years.<br />
Several differences exist between <strong>read<strong>in</strong>g</strong> <strong>text</strong> <strong>in</strong> documents <strong>and</strong> <strong>in</strong> <strong>scene</strong> <strong>images</strong>.<br />
A few examples are shown <strong>in</strong> Figure 1.2. The first primary difference is <strong>in</strong> the problem<br />
of locat<strong>in</strong>g the <strong>text</strong> to be recognized. In a st<strong>and</strong>ard one or two column document,<br />
almost no <strong>detection</strong> must be done. L<strong>in</strong>es of <strong>text</strong> are easy to identify <strong>and</strong> simple<br />
heuristics are usually sufficient to prepare the <strong>in</strong>put <strong>for</strong> <strong>recognition</strong>. In more complex<br />
documents such as newspapers <strong>and</strong> magaz<strong>in</strong>es, there is an added difficulty of dist<strong>in</strong>-<br />
2