a laplacian method for video text detection
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A Laplacian Method for Video Text Detection. Trung Quy Phan, Palaiahnakote Shivakumara and Chew Lim Tan. Agenda . Introduction Previous Methods Laplacian Method Experimental Results Conclusion and Future Work. Agenda . Introduction Previous Methods Laplacian Method Experimental Results

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a laplacian method for video text detection

A Laplacian Methodfor Video Text Detection

Trung Quy Phan, Palaiahnakote Shivakumara and Chew Lim Tan

agenda
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
  • Conclusion and Future Work
agenda1
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
  • Conclusion and Future Work
introduction
Introduction
  • Motivation: video indexing
    • Generates keywords from text
    • Able to retrieve a particular event or image
  • Graphic & scene text
  • Different from camera-based images
    • Low resolution
    • Complex background
    • Text movement & distortion
agenda2
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
  • Conclusion and Future Work
previous methods
Previous Methods
  • Connected component-based
    • Assumes text pixels have the same colors or grayscale intensities
  • Edge-based
    • Works well for high contrast text
    • Produces false positives for complex backgrounds
  • Texture-based
    • Trainable
    • Computationally expensive
agenda3
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
  • Conclusion and Future Work
laplacian method
Laplacian Method
  • Step 1: Text Detection
    • Identifies candidate text regions
  • Step 2: Boundary Refinement
    • Refines the text block boundaries
  • Step 3: False Positive Elimination
agenda4
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
    • Text Detection
    • Boundary Refinement
    • False Positive Elimination
  • Experimental Results
  • Conclusion and Future Work
text detection
Text Detection
  • Text regions have a large number of discontinuities
  • Input  grayscale  Laplacian-filtered to detect the discontinuities in four directions
text detection1
Text Detection
  • Text regions typically have many positive and negative peaks of large magnitudes
text detection2
Text Detection
  • Maximum gradient difference (MGD) [1]
    • For each 1 × N window, MGD is the difference between the maximum and minimum values
  • Text regions have larger MGD values because of the peaks of large magnitudes
text detection3
Text Detection
  • K-means clustering on the MGD map
    • K = 2, Euclidean distance
agenda5
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
    • Text Detection
    • Boundary Refinement
    • False Positive Elimination
  • Experimental Results
  • Conclusion and Future Work
boundary refinement
Boundary Refinement
  • Binary Sobel edge map SM of the input image (only for text regions)
  • Horizontal and vertical projection profiles
boundary refinement1
Boundary Refinement
  • Horizontal
  • Vertical
agenda6
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
    • Text Detection
    • Boundary Refinement
    • False Positive Elimination
  • Experimental Results
  • Conclusion and Future Work
false positive elimination
False Positive Elimination
  • Text block: (1) aspect_ratio ≥ T1 and (2) edge_area / area ≥ T2
    • edge_area = number of edge pixels
  • Otherwise, false positive
  • T1 = 0.5 and T2 = 0.1
false positive elimination2
False Positive Elimination
  • 1st false positive removed due to the aspect ratio rule
false positive elimination3
False Positive Elimination
  • Sobel edge map
  • 2nd false positive removed due to the edge density rule
agenda7
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
  • Conclusion and Future Work
experimental results
Experimental Results
  • 101 images: news, sports, movies, etc.
  • Sizes from 320 × 240 to 816 × 448
  • English, Chinese and Korean text
  • Three implemented methods: edge-based method [1], gradient-based method [2] and uniform-colored method [3]
agenda8
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
    • Sample Results
    • Performance Measures
    • Evaluation
  • Conclusion and Future Work
sample results
Sample Results
  • Low contrast text

Input Edge-based Gradient-based

Uniform-colored Proposed

sample results1
Sample Results
  • Scene text

Input (from [4]) Edge-based Gradient-based

Uniform-colored Proposed

sample results2
Sample Results
  • The proposed method fails if the contrastis too low

Input Proposed

Edge-based Gradient-based Uniform-colored

sample results3
Different font sizes

Different languages

Sample Results

(from [4])

sample results4
Sample Results
  • Different window sizes
  • N = 5 in our experiment

Input

N = 5 N = 21

agenda9
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
    • Sample Results
    • Performance Measures
    • Evaluation
  • Conclusion and Future Work
performance measures
Performance Measures
  • Detection Rate (DR)
    • number of localized text / number of text
  • False Positive Rate (FPR)
    • number of non-text /number of localized blocks
  • Misdetection Rate (MDR)
    • number of text with missing characters / number of localized text

DR = 100%

FPR = 25%

MDR = 67%

agenda10
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
    • Sample Results
    • Performance Measures
    • Evaluation
  • Conclusion and Future Work
evaluation1
Evaluation
  • The proposed method outperforms the edge-based and gradient-based methods in all performance measures
evaluation2
Evaluation
  • Compared to the gradient-based method, the proposed method has a slightly worse MDR but a significantly higher DR
agenda11
Agenda
  • Introduction
  • Previous Methods
  • Laplacian Method
  • Experimental Results
  • Conclusion and Future Work
conclusion and future work
Conclusion and Future Work
  • The proposed method performs well on the dataset
    • Gradient information  candidate text regions
    • Edge information  localized text blocks
  • May fail if the contrast is too low
  • Can be extended for non-horizontal text
references
References
  • C. Liu, C. Wang and R. Dai, “Text Detection in Images Based on Unsupervised Classification of Edge-based Features”, ICDAR 2005, pp. 610-614.
  • E. K. Wong and M. Chen, “A new robust algorithm for video text extraction”, Pattern Recognition 36, 2003, pp. 1397-1406.
  • V. Y. Mariano and R. Kasturi, “Locating Uniform-Colored Text in Video Frames”, 15th ICPR, Volume 4, 2000, pp 539-542.
  • X. S. Hua, W. Liu and H. J. Zhang, “Automatic Performance Evaluation for Video Text Detection”, ICDAR, 2001, pp 545-550.
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