unified detection and recognition for reading text in scene images

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6.4.2.1 Model Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 6.4.2.2 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.4.4 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.4.5 End-to-End Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7. SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 xii
LIST OF TABLES Table Page 1.1 Difficulties of text localization in various media . . . . . . . . . . . . . . . . . . . . . . . 6 3.1 Sign detection results with MPM prediction . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Sign detection results at the region-level equal error rate . . . . . . . . . . . . . 47 4.1 Impact of similarity on recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2 Impact of using similarity in a separate recognition stage . . . . . . . . . . . . . 70 4.3 Word and character accuracy with various forms of the model . . . . . . . . . 72 6.1 Comparison of word errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.2 Comparison between the end-to-end system and OmniPage. . . . . . . . . . . 129 xiii
Page 1 and 2: UNIFIED DETECTION AND RECOGNITION F
Page 3 and 4: UNIFIED DETECTION AND RECOGNITION F
Page 5 and 6: ACKNOWLEDGEMENTS As soon as one beg
Page 7 and 8: ABSTRACT UNIFIED DETECTION AND RECO
Page 9 and 10: CONTENTS Page ACKNOWLEDGEMENTS . .
Page 11: 4.3.2.1 Model Training . . . . . .
Page 15 and 16: 3.11 Visual comparison of local and
Page 17 and 18: CHAPTER 1 INTRODUCTION The first at
Page 19 and 20: Figure 1.2. Images for document pag
Page 21 and 22: T he P h oto S p e cialists Input O
Page 23 and 24: Figure 1.4. Small text in an image
Page 25 and 26: section, we review some of the appr
Page 27 and 28: Kusachi et al. [62] have a multi-re
Page 29 and 30: 1.3.3 Adaptive Recognition Several
Page 31 and 32: ather than integrated systems. In a
Page 33 and 34: Background Faces Allen Andrew Keith
Page 35 and 36: CHAPTER 2 DISCRIMINATIVE MARKOV FIE
Page 37 and 38: If we have a family of sets C = {C
Page 39 and 40: ̂θ ≡ arg max p (θ | D, I) (2.6
Page 41 and 42: functions, i.e., θ = [ θ A θ ] B
Page 43 and 44: P L (θ; α) = −α ‖θ‖ 1 (2.
Page 45 and 46: we drop the dependence of the messa
Page 47 and 48: CHAPTER 3 TEXT AND SIGN DETECTION B
Page 49 and 50: For example, since neighboring regi
Page 51 and 52: Figure 3.3. Decomposition of images
Page 53 and 54: 3.3.1 Feature Overview Rather than
Page 55 and 56: • Raw pixel statistics (e.g., ran
Page 57 and 58: 3.4.2 Experimental Procedure For ou
Page 59 and 60: Figure 3.7. Example contextual dete
Page 61 and 62: Table 3.1. Sign detection results w
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Table 3.2. Sign detection results a
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In conclusion, the “default” pr
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CHAPTER 4 UNIFYING INFORMATION FOR
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will outline the details of our mod
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y 1 01 01 01 0 0 01 1 1 0 0 01 1 1
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4.2.2 Language Model Properties of
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5 Basis Functions Learned Function
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node for the factor C, while w B is
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Figure 4.5. Examples of sign evalua
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Lexicon The lexicon we use is deriv
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40 40 Number of Queries 30 20 10 Nu
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and could be modeled directly if we
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Figure 4.9. Examples from the sign
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Model Correct Free checking 31 BOLT
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4.3.4.2 Lexicon Model Here we discu
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CHAPTER 5 UNIFYING DETECTION AND RE
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character and these were directly u
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L C ( θ C ; F, D ) ≡ ∑ k log p
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For the independent method, each ca
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Wavelet Transform Scale 0.3 0.25 0.
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Scale −0.02 0 0.02 0.04 0.06 0.08
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Figure 5.6. Sample images used in e
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1 Avg. Categ. Accuracy 0.95 0.9 0.8
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Relative Improvement 0.5 0.4 0.3 0.
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Recognition Gain 1 0.8 0.6 0.4 0.2
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of further special recognition. Hav
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CHAPTER 6 THE ROBUST READER In the
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6.2 Semi-Markov Model for Segmentat
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6.2.1.2 Character Bigrams As in ear
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exponential of the corresponding Ma
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is signalled, and this string may e
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and k. This joint space over i and
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each location, all the rectified fi
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This procedure is outlined visually
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(e.g., cs, Bk, Kr, Nb, pd, Tl), we
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18.8 18.6 18.4 KL N−Best Ratio Er
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σ Image Output Binarized OmniPage
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F I B E IA A R T C E N T E R (a) F
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6.4.5 End-to-End Demonstration In t
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T he P h oto S p e cialists The Pro
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T he P hoto S p ecialists L L O Y D
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Table 6.2. Comparison of recognitio
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mercial document recognition softwa
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In addition to the models, we have
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[12] Blum, Avrim, and Langley, Pat.
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[37] Geman, Stuart, and Geman, Dona
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[63] Lafferty, John, McCallum, Andr
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[89] Pal, Chris, Sutton, Charles, a
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[114] Torralba, Antonio, Murphy, Ke
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[139] Zhou, Yaqian, Wenb, Fuliang,
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unified detection and recognition for reading text in scene images

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