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 elimination1
False Positive Elimination

  • 2 false positives


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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