A New Robust Algorithm for Video Text Extraction

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1 A New Robust Algorithm for Video Text Extraction Pattern Recognition, vol. 36, no. 6, June 2003 Edward K. Wong and Minya Chen School of Electrical Engineering and Computer Science Kyungpook National Univ.

2 Algorithm Detecting potential text line segments from horizontal scan line Expanding and merging from adjacent scan lines to form text block Designed for texts that are super-imposed on the video Use of a scan line approach Fast filtering of non-text scan-line video data 2 / 22

3 Abstract A robust algorithm for extracting text Computing maximum gradient difference Detecting potential text line segment from horizontal scan line of video Expanding or combining with potential text line segments from adjacent scan lines Filtering and refinement Using color information for more precise location 3 / 22

4 1. Introduction Certain common characteristics of text in video High contrast with the background Uniform color and intensity Horizontally aligned Remaining stationary in a series of consecutive video frames A unique characteristic of the algorithm Use of a scan-line approach that allows fast filtering of scan-line video data does not contain text 4 / 22

5 2. Prior and Related Work Text detection algorithm for binary images Detecting text in color / video images Increased interest in multimedia technology Rich in color content, low spatial freq. and noise Output of text detection algorithms Rectangular boxes or regions contain the text characters Highlighting Binary maps explicitly contain text pixels OCR 5 / 22

6 3. New Text Extraction Algorithm The algorithm Identify potential text line segments Text blocks detection Text blocks filtering Boundary adjustments Bi-color clustering Artifact filtering Contour smoothing 6 / 22

7 Step 1: Identify potential text line segments Computing the horizontal luminance gradients dx by using the mask [-1 1] At each pixel location, computing the maximum gradient difference (MGD) within a local window of size n X 1 centered at the pixel (n = 21) Fig. 1 Test image data13. 7 / 22

8 Typically, text regions have large MGD value and background regions have small MGD values Fig. 2 Illustration of a scan line segment. Fig. 3 Gradient profile for scan line for test image. 8 / 22

9 Thresholding the computed MGD values to obtain one or more continuous segments on the scan line Computing the mean and variance of the horizontal distances on the gradient profile Fig. 4 Binary text extracted for test image. 9 / 22

10 Step 2: Text block detection Expanding or merging with text line segments from adjacent scan lines to form text blocks Top-down pass The group of pixels immediately below the pixels of each potential text line segments Merging if the mean and variance of the grayscale values are close Bottom-up pass Pixels immediately above a potential text line segment or an expanded text line segment 10 / 22

11 Step 3: Text block filtering Based on the area and height-to-width ratio Discarding the text block fall outside some predefined ranges Eliminating regions that look like text Step 4: Boundary adjustments Adjusting the boundary to include text pixels that lie outside the boundary Computing the average MGD value of the text block Adjusting the boundary at each of the four sides of the text block that are close 11 / 22

12 Step 5: Bi-color clustering Using the color information contained in a video More precisely locating the foreground text pixels Applying bi-color clustering Color histogram within the text block Picking two peak values; initial colors for clustering process 12 / 22

13 Step 6: Artifact filtering Eliminating non-text noisy artifacts Using a connected component labeling algorithm Filtering procedures If text_block_height is greater than threshold and the area is greater than total_text_area/2, discard the entire text block If the area of a connected component is less than threshold, it is discarded If a connected component touches one of the four sides of the text block, and its size is larger than a certain threshold, it is discarded 13 / 22

14 Step 7: Contour smoothing Smoothing the contours by pruning one-pixel thick side branches (or artifact) from the contours Using the classical pruning structuring element pairs Fig. 5 Classical pruning structuring elements. 14 / 22

15 Example 1 4. Experimental Results and Performance Fig. 6 Test image. Fig. 7 MGD for test image. 15 / 22

16 Example 1 Fig. 8 Detected text blocks. Fig. 9 Extracted binary text. 16 / 22

17 Example 2 Test image with varying background Fig. 10 Test image. Fig. 11 Extracted binary text. 17 / 22

18 Evaluating performance Recall (detection rate); 88.9% Precision; 95.7% JPEG compression and decompression Fig. 12 Test image after JPEG compression-decompression. Fig. 13 Extracted text. 18 / 22

19 Fig. 14 Test image with Gaussian noise (SNR=30). Fig. 15 SNR=20. Fig. 16 SNR=10. Fig. 17 Extracted text (SNR=30). Fig. 18 SNR=20. Fig. 19 SNR= / 22

20 TABLE 1 Recall and precision for the original, JPEG, with Gaussian noise TABLE 2 Recall and precision for salt and pepper (SAP), speckle noise 20 / 22

21 TABLE 3 Performance measure for various text detection algorithms 21 / 22

22 5. Discussion and Concluding Remarks A new robust algorithm for extracting text from color video Output; consist of connected components of text character pixels OCR JPEG compressed-decompressed images Corrupted with various noises A unique characteristic of the algorithm; scan line approach 22 / 22

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