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Con-Text: Text Detection Using Background Connectivity for Fine-Grained Object Classification. Sezer Karaoglu, Jan van Gemert, Theo Gevers. Can we achieve a better object recognition with the help of scene-text ?. Goal.

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con text text detection using background connectivity for fine grained object classification

Con-Text: Text Detection Using Background Connectivity for Fine-Grained Object Classification

Sezer Karaoglu, Jan van Gemert, Theo Gevers

slide3
Goal
  • Exploit hidden details by text in the scene to improve visual classification of very similar instances.

DJ SUBS Breakfast

Starbucks Coffee

StarbucksCoffee

SKY

SKY

SKY

CAR

CAR

Application : Linking images from Google street view to textual business inforation as e.g. the Yellow pages, Geo-referencing, Information retrieval

challenges of text detection in natural scene images
Challenges of Text Detection in Natural Scene Images
  • Lighting
  • Surface Reflections
  • Unknown background
  • Non-Planar objects
  • Unknown Text Font
  • Unknown Text Size
  • Blur
literature review text detection
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
motivation to remove background for text detection
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.
propose d text detection method
Proposed Text Detection Method

Text detection by BG

substraction

Automatic BG seed selection

BG reconstruction

background seed selection
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 Image

Color Boosting

Contrast

Objectness

conditional dilation for bg connectivity
ConditionalDilationfor 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

text recognition experiment s
Text Recognition Experiments
  • ICDAR’03 Dataset with 251 test images, 5370 characters, 1106 words.
icdar 2003 dataset char recognition results
ICDAR 2003 DatasetChar. Recognition Results

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.

imagenet dataset
ImageNet Dataset

Bakery

Country House

Discount House

Funeral

Pizzeria

Steak

  • 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 resultsobtained 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.
fine grained building classification results
Fine-GrainedBuildingClassificationResults

ocr : 15.6 ± 0.4

Fusion

Text

Visual

Bow + ocr : 39.0 ± 2.6

Bow : 32.9 ± 1.7

Discount House

Visual

#269

#431

#584

#2752

Text

#1

#4

#5

#8

Proposed

#1

#4

#5

#8

conclusion
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.

  • Improvedperformance to scene

text recognition.

  • Improved Fine-Grained Object

Classification performance

with visual and scene text

information fusion.

slide15
DEMO

TRY HERE

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