1. Con-Text: Text Detection Using Background Connectivity for Fine-Grained Object Classification Sezer Karaoglu, Jan van Gemert, Theo Gevers 1

2. Can we achieve a better object recognition with the help of scene-text? 2

3. Goal Exploit hidden details by text in the scene to improve visual classification of very similar instances. Application : Linking images from Google street view to textual business inforation as e.g. the Yellow pages, Geo-referencing, Information retrieval 3 SKY CAR DJ SUBS BreakfastStarbucks Coffee

4. Challenges of Text Detection in Natural Scene Images o Lighting o Surface Reflections o Unknown background o Non-Planar objects o Unknown Text Font o Unknown Text Size o Blur 4

5. Literature Review Text Detection Texture Based: Wang et al. “End-to-End Scene Text Recognition” ICCV ‘11 Computational Complexity Dataset specific Do not rely on heuristic rules Region Based: Epshtein et al. “Detecting Text in Natural Scenes with Stroke Width Transform ” CVPR ‘10 Hard to define connectivity Segmentation helps to improve ocr performance 5

6. Motivation to remove background for Text Detection To reduce majority of image regions for further processes. To reduce false positives caused by text like image regions (fences, bricks, windows, and vegetation). To reduce dependency on text style.

7. 7 Automatic BG seed selectionBG reconstruction Text detection by BG substraction Proposed Text Detection Method

8. Background Seed Selection Color, contrast and objectness responses are used as feature. Random Forest classifier with 100 trees based on out-of-bag error are used to create forest. Each tree is constructed with three random features. The splitting of the nodes is made based on GINI criterion. Original ImageColor BoostingContrastObjectness

9. Conditional Dilation for BG connectivity where B is the structring element (3 by-3 square), M is the binary image where bg seeds are ones and X is the gray level input image until repeat

10. Text Recognition Experiments ICDAR’03 Dataset with 251 test images, 5370 characters, 1106 words. 10

11. ICDAR 2003 Dataset Char. Recognition Results 11 Method Cl. Rate (%) ABBYY36 Karaoglu et. al.62 Proposed63 The proposed system removes 87% of the non-text regions where on average 91% of the test set contains non-text regions. It retains approximately %98 of text regions.

12. ImageNet Dataset ImageNet building and place of business dataset ( 24255 images 28 classes, largest dataset ever used for scene tekst recognition) The images do not necessarily contain scene text. Visual features : 4000 visual words, standard gray SIFT only. Text features: Bag-of-bigrams, ocr results obtained for each image in the dataset. 3 repeats, to compute standard deviations in Avg. Precision. Histogram Intersection Kernel in libsvm. Text only, Visual only and Fused results are compared. Steak PizzeriaFuneralBakeryDiscount HouseCountry House

13. Fine-Grained Building Classification Results ocr : 15.6 ± 0.4 Bow : 32.9 ± 1.7 TextVisual Fusion Bow + ocr : 39.0 ± 2.6 #269#431#584#2752 #1#4#5#8 Visual Text Proposed Discount House #1#4#5#8

14. Conclusion Background removal is a suitable approach for scene text detection A new text detection method, using background connectivity and, color, contrast and objectness cues is proposed. Improved performance to scene text recognition. Improved Fine-Grained Object Classification performance with visual and scene text information fusion. 14

15. DEMO TRY HERE