Date post: | 26-May-2015 |
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Engineering |
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SCENE TEXT RECOGNITION IN MOBILE APPLICATION BY
CHARACTER DESCRIPTOR AND STRUCTURE COMFIGURATION
CHERIYAN K M
INTRODUCING….. Valuable information form an image. To extract an information.
Automatic and Effective scene text detection. Recognition algorithm.
Factors affecting on extraction. Cluttered background. Difference in text pattern.
Difficult to model the structure of character. Lake of discriminative pixel level appearance. Structure features from non-text background
outliers. Different word , may diff. characters , in various
fonts , styles and size.
Two activities; Text detection.
Localize the image region containing the text characters. Based on
Color uniformity and Horizontal alignment of text char.
Text recognition. Transform pixel-based text into reliable codes. Distinguish diff. text characters , Properly compose the
text word. 62 identity category of text characters.
9 (0-9)26 (a-z)26 (A-Z)
Two schemes; Character recognizer to predict the category of text char. Binary character classifier to predict the existence of
ctgry.
RELATED WORKS
Optical Character Recognizer (OCR) system. Many algorithms are proposed;
Weinmen:- combined the Gabor-based appearance model.
Neumann:- based on extremal region. Smith:- based on SIFT. Mishra:- adopted conditional random field. Lu:- modeled the inner character structure. Coates:- extracted local features of character
patches.
LAYOUT BASED SCENE TEXT DETECTION A text;
Instruction Identifier Uniform color Aligned arrangement
Two processes are employed to complete layout analysis
1. Color Decomposition2. Horizontal Alignment
Improved to compatible with mobile app
LAYOUT ANALYSIS OF COLOR DECOMPOSITION
Boundary clustering algorithm base on bigram color uniformity.
Group pixels of same color into a layer. Character boundary boarder b/w txt and bg.
(color pair) Create a vector of color pair (txt and bg).
LAYOUT ANALYSIS OF HORIZONTAL ALIGNMENT
Text information(string)
Several character members
In similar size
Approximately horizontal alignment
The geometrical properties to detect the existence of text characters
Adjacent character grouping algorithm
Bounding box>siblings>similar size & vertical location>merge
For non-horizontal strings-> ±/6 degree set as range.
STRUCTURE BASED SCENE TEXT RECOGNITION
To extract text information. Binary classification problem. Character classes(Queried characters). Binary classifier:- to distinguish character
class from other classes or bg outliers. Eg: Character class A predict patches containing
A as positive. And other as negative. Two activities;
1. Character descriptor.2. Stroke configuration.
CHARACTER DESCRIPTOR
Extract structure features. 4 different key points features;
1. Harris Detector:- To extract Key points from corner and junction.
2. MSER Detector:-To extract Key point from stroke component.
3. Dense Detector:- To extract Key point uniformly.4. Random Detector:- To extract the preset
number of Key points in a random pattern.
Flowchart of our proposed character descriptor
HOG:-features are Calculated as observed feature vector x.(Histogram of Oriented Gradient)
•Selected as local feature descriptor( compatibility with all 4 key point detectors).
SIFT and SURF are not employed Normalization of character patches(128x128). Feature Quantization: to aggregate the
extracted features Bag-of-Words(BOW) Medel:- Applied to key points
from all 4 feature detector. Gaussian Mixture Model(GMM):Applied to key
points from DD & RD.(fixed number and location of key point)
Now mapping both into characteristic Histogram as feature representation.
Cascading BOW and GMM-based feature repr. ,we get Character Descriptor.
CHARACTER STROKE CONFIGURATION
Stroke:- Region bounded by two parallel boundary segments.
Stroke width Stroke orientation Characters are connected strokes with
configuration. Structure Map of Strokes is stroke
configuration.( is consistant)Eg: B have 1 vertical stroke
2 arc strokes.
B
Synthesized character generator: Estimate stroke configuration from computer s/w.(Provide accurate skeleton and boundary)
Synthetic font training dataset(20000 are selected out off 67400 character patches) Contain 62 class of characters(128x128 pixel)
Compose Stroke ConfigurationStep1
Discrete Contour Evaluation(DCE):obtain boundary and skeleton. Skeleton pruning on the basis of DCE.
DCE simplifies the character(using polygon and small no. of vertices)
DCE and Skeleton pruning are invariant to deformation and scaling.
Step2 Estimate stroke width and orientation
Width: length along normal Orientation: tangent
Sampling from character boundary 128 samples. So that no. of samples = length Estimating Taking two neighboring sample point to fit a
line. Approximately collinear. A quadratic curve.
Step3 Calculate Skeleton-based stroke map Consistency of stroke width and orientation.
Construct stroke section: If sample point satisfying the stroke related features.
Construct junction sections: If they are not. Skeleton points are extracted.
Width no larger than 3 Orientation no larger than /8
STROKE ALIGNMENT METHOD
To handle various fonts, styles …..etc Mean value of all stroke configuration.
Mean value,
D=Distance b/w stroke configurations of two samples
S=Mean value of stroke configurations. Ti=Transformations applied on strokes of i-th
stroke configuration. g(Ti)=Amplitude of the transformation.
DEMO SYSTEM
THANK YOU