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On Combining Multiple Segmentations in Scene Text Recognition

On Combining Multiple Segmentations in Scene Text Recognition. Lukáš Neumann and Ji ří Matas Centre for Machine Perception, Department of Cybernetics Czech Technical University, Prague. T alk Overview. End-to-End Scene Text Recognition - Problem Introduction The TextSpotter System

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On Combining Multiple Segmentations in Scene Text Recognition

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  1. On Combining Multiple Segmentations in SceneText Recognition Lukáš Neumann and Jiří Matas Centre for Machine Perception, Department of Cybernetics Czech Technical University, Prague

  2. Talk Overview • End-to-End Scene Text Recognition - Problem Introduction • The TextSpotter System • Character Detection as Extremal Region (ERs) Selection • Line formation & Character Recognition • Character Ordering • Optimal Sequence Selection • Experiments

  3. End-to-End Scene Text Recognition Input: Digital image (BMP, JPG, PNG) / video (AVI) Lexicon-free method Output: Set of words in the imageword = (horizontal)rectangular bounding box, text content Bounding Box=[240;1428;391;1770] Content="TESCO"

  4. System Overview • Multi-scale Character Detection [1] with Gaussian Pyramid (new) • Text Line Formation [2] • Character Recognition [3] • Optimal Sequence Selection (new) [1] L. Neumann, J. Matas, “Real-time scene text localization and recognition”, CVPR 2012 [2] L. Neumann, J. Matas, “Text localization in real-world images using efficiently pruned exhaustive search”, ICDAR 2011 [3] L. Neumann, J. Matas, “A method for text localization and recognition in real-world images”, ACCV 2010

  5. Character Detection - Thresholding Input image (PNG, JPEG, BMP) 1Dprojection <0;255> (grey scale, hue,…) Extremal regions with threshold ( =50, 100, 150, 200)

  6. Extremal Regions (ER) • Let image I be a mapping I: Z2 S Let S be a totally ordered set, e.g. <0, 255> • Let A be an adjacency relation (e.g. 4-neigbourhood) • Region Q is a contiguous subset w.r.t. A • (Outer) Region Boundary δQ is set of pixels adjacent but not belonging to Q • Extremal Region is a region where there exists a threshold that separates the region and its boundary : pQ,qQ : I(p) <   I(q)  = 32 • Assuming character is an ER, 3 parameters still have to be determined: • Threshold • Mapping to a totally order set (colour space projection) • Adjacency relation

  7. ER Detection - Threshold Selection • Character boundaries are often fuzzy • It is very difficult to locally determine the threshold value, typical document processing pipeline (image binarization OCR) leads to inferior results • Thresholds that most probably correspond to a character segmentation are selected using a CSER classifier [1], multiple hypotheses for each character are generated [1] L. Neumann and J. Matas, “Real-time scene text localization and recognition”, CVPR 2012

  8. ER Detection – Threshold Selection • p(r|character) estimated at each threshold for each region • Only regions corresponding to local maxima selected by the detector • Incrementally computed descriptors used for classification [1] • Aspect ratio • Compactness • Number of holes • Horizontal crossings • Trained AdaBoost classifier with decision trees calibrated to output probabilities • Linear complexity, real-time performance (300ms on an 800x600px image) [1] L. Neumann and J. Matas, “Real-time scene text localization and recognition”, CVPR 2012

  9. ER Detection - Color Space Projection • Color space projection maps a color image into a totally ordered set • Trade-off between recall and speed (although can be easily parallelized) • Standard channels (R, G, B, H, S, I) of RGB / HSI color space • 85.6% characters detected in the Intensity channel, combining all channels increases the recall to 94.8% Source Image Intensity Channel (no threshold exists for the letter “A”) Red Channel

  10. ER Detection - Gaussian Pyramid • Pre-processing with a Gaussian pyramid alters the adjacency relation • At each level of the pyramid only a certain interval of character stroke widths is amplified • Not a major overhead as each level is 4 times faster than the previous one, total processing takes ~ 4/3 of the first level (1 + ¼ + ¼2…) Characters formed of multiple small regions Multiple characters joint together

  11. Character Recognition • Regions agglomerated into text lines hypotheses by exhaustive search [1] • Each segmentation (region) labeled by a FLANN classifier trained on synthetic data [2] • Multiple mutually exclusive segmentations with different label(s) present in each text line hypothesis P ilI n f f m A n [1] Neumann, Matas, Text localization in real-world images using efficiently pruned exhaustive search, ICDAR 2011 [2] Neumann, Matas, A method for text localization and recognition in real-world images”, ACCV 2010

  12. Character Ordering • Region A is a predecessor of a region B if A immediately precedes B in a text line • Approximated by a heuristic function based on text direction and mutual overlap • The relation induces a directed graph for each text line

  13. Optimal Sequence Selection • The final region sequence of each text line is selected as an optimal path in the graph, maximizing the total score • Unary terms • Text line positioning (prefers regions which “sit nicely” in the text line) • Character recognition confidence • Binary terms (regions pair compatibility score) • Threshold interval overlap (prefers that neighboring regions have similar threshold) • Language model transition probability (2nd order character model) Accommodation

  14. Experiments ICDAR 2011 Dataset – Text Localization [1] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform”, CVPR 2010

  15. Experiments ICDAR 2011 Dataset – Text Localization [1] C. Shi, C. Wang, B. Xiao, Y. Zhang, and S. Gao, “Scene text detection using graph model built upon maximally stable extremalregions”, Pattern Recognition Letters, 2013 [2] A. Shahab, F. Shafait, and A. Dengel, “ICDAR 2011 robust reading competition challenge 2: Reading text in scene images”, ICDAR 2011 [3] L. Neumann and J. Matas, “Real-time scene text localization and recognition”, CVPR 2012 [4] C. Yi and Y. Tian, “Text string detection from natural scenes by structure-based partition and grouping”, Image Processing, 2011 [5] S. M. Hanif and L. Prevost, “Text detection and localization in complex scene images using constrained adaboostalgorithm”, ICDAR 2009

  16. Experiments ICDAR 2011 Dataset – End-to-End Text Recognition Percentage of words correctly recognized without any error – case-sensitive comparison (ICDAR 2003 protocol) [1] L. Neumann and J. Matas, “Real-time scene text localization and recognition”, CVPR 2012

  17. Sample Results on the ICDAR 2011 Dataset FREEDON chips cut CABOT PLACF

  18. Conclusions • Multi-scale processing / Gaussian Pyramid improves text localization results without a significant impact on speed • Combining several channels and postponing the decision about character detection parameters (e.g. binarization threshold) to a later stage improves localization and OCR accuracy • Method current state • The method placed second in ICDAR 2013 Text Localization competition, 1.4% worse than the winner (f-measure)(unfortunately, end-to-end text recognition is not part of the competition) • Online demo available at http://www.textspotter.org/ • OpenCV implementation of the character detector in progress by the open source community • Future work • OCR accuracy improvement • Overcoming limitations of CC-based methods (e.g. non-linearity non-robustness caused by a single pixel)

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