End-to-End Text Recognition with Convolutional Neural Networks Scene Text Recognition Overview Unsupervised Feature Learning Synthetic Data

End-to-End Text Recognition with Convolutional Neural Networks Scene Text Recognition Overview Unsupervised Feature Learning Synthetic Data www.phwiki.com

End-to-End Text Recognition with Convolutional Neural Networks Scene Text Recognition Overview Unsupervised Feature Learning Synthetic Data

Bellis, Bill, Meteorologist has reference to this Academic Journal, PHwiki organized this Journal End-to-End Text Recognition with Convolutional Neural Networks Tao Wang, David J. Wu, Adam Coates, Andrew Y. Ng Computer Science Department Stan as long as d University Denotes equal contribution Scene Text Recognition Overview Text “in the wild” are hard to recognize Wide range of variations in backgrounds, textures, fonts, in addition to lighting conditions Street View Text Dataset K.Wang et al., 2011 ICDAR 2003 Dataset S. Lucas et al., 2003 Two-Stage Framework Detection/Classification High-level Inference “HOTEL”

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Exhaustive Graph Search MSER + SVM with RBF Kernel Neumann in addition to Matas, 2012 CRF + N-gram model HOG + SVM with RBF Kernel Mishra et al., 2012 Pictorial Structure HOG + R in addition to om Ferns K. Wang et al., 2011 Semi-Markov CRF Appearance + Geometry Weinman et al., 2008 High-level inference Classification in addition to detection Works Simple off-the-shelf heuristics Learnt features + 2-layer CNN Our approach Graph based inference models H in addition to -designed features + off-the-shelf classifier Most other approaches High-level inference Classification in addition to detection Various Benchmarks Detection/Classification End-to-end system after high-level inference ICDAR in addition to SVT end-to-end text recognition SOTA SOTA on ICDAR SOTA

Unsupervised Feature Learning Contrast Normalization + ZCA whitening K-Means Coates et al., 2011 Convolution Convolution Backpropagation Large representation but not enough data. Overfitting ~10K parameters as long as detection ~50K parameters as long as classification Synthetic Data Color Statistics Synthetic “hard negatives” Real Synthetic Unrealistic Synthetic Data Real Data Java.Font + Natural backgrounds

Detector Per as long as mance Text Line Bounding boxes C in addition to idate spaces 83.9 62-way classification accuracy on ICDAR cropped characters (on ICDAR-Sample characters) Accuracy(%) Higher is better Classifier Per as long as mance

Sliding window position Char Class Word Recognition Lexicon: MAKE SERIES ESTATE POKER S E R I E S -5.45 7.82 -1.74 -9.02 max

Cropped Word Recognition Accuracy Accuracy(%) Cropped Words Benchmarks Higher is better C in addition to idate spaces generated by detector

End-to-end text recognition results F-Score End-to-end Benchmarks Higher is better Sample Output Images from SVT Sample Output Images from ICDAR-FULL

– “confidence margin” PEOSTEL PEOST POST POS Hunspell POSE POST PEOPLE PISTOL LEXICON Suggested Words Our F-score: 0.38 Neumann in addition to Matas, 2010: 0.40 c Conclusion Learnt features + 2-layer CNN as long as + character detection in addition to cla

Bellis, Bill ABC15 News at 4 PM - KNXV-TV Meteorologist www.phwiki.com

Bellis, Bill Meteorologist

Bellis, Bill is from United States and they belong to ABC15 News at 4 PM – KNXV-TV and they are from  Phoenix, United States got related to this Particular Journal. and Bellis, Bill deal with the subjects like Meteorology

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