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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5.8 Average <strong>recognition</strong> accuracy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92<br />
5.9 Relative improvement of the factored over <strong>in</strong>dependent method . . . . . . . 93<br />
5.10 Relative ga<strong>in</strong>s of features <strong>for</strong> different tasks . . . . . . . . . . . . . . . . . . . . . . . . . 94<br />
5.11 Scatterplot of normalized feature ga<strong>in</strong>s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95<br />
6.1 Examples signs that make prior word segmentation difficult . . . . . . . . . . 100<br />
6.2 Broken characters from a st<strong>and</strong>ard segmentation algorithm . . . . . . . . . . . 100<br />
6.3 Example <strong>text</strong> str<strong>in</strong>g segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102<br />
6.4 Dynamic programm<strong>in</strong>g variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104<br />
6.5 Partial f<strong>in</strong>ite state word graph. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106<br />
6.6 Rectification of even <strong>and</strong> odd steerable pyramid filter outputs . . . . . . . . 110<br />
6.7 Pre-process<strong>in</strong>g <strong>for</strong> <strong>recognition</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112<br />
6.8 Synthetic font tra<strong>in</strong><strong>in</strong>g data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114<br />
6.9 Comparison of beam search strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117<br />
6.10 Comparison of <strong>recognition</strong> methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117<br />
6.11 Example <strong>in</strong>put <strong>and</strong> b<strong>in</strong>arized <strong>images</strong> with OmniPage output . . . . . . . . . 118<br />
6.12 Example <strong>recognition</strong> comparison at lower resolutions . . . . . . . . . . . . . . . . 119<br />
6.13 Examples of failed segmentation <strong>and</strong>/or <strong>recognition</strong> . . . . . . . . . . . . . . . . . 121<br />
6.14 Example <strong>text</strong> <strong>detection</strong> probabilities <strong>and</strong> c<strong>and</strong>idate regions . . . . . . . . . . . 124<br />
6.15 Recognition of c<strong>and</strong>idate regions detected at multiple scales . . . . . . . . . . 125<br />
6.16 Example <strong>scene</strong> <strong>images</strong> used <strong>for</strong> qualitative comparison. . . . . . . . . . . . . . . 126<br />
6.17 Example output of the end-to-end system . . . . . . . . . . . . . . . . . . . . . . . . . . 127<br />
6.18 Example detected <strong>text</strong> regions from OmniPage . . . . . . . . . . . . . . . . . . . . . 128<br />
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