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Recognition of Video Text Through Temporal Integration

Recognition of Video Text Through Temporal Integration. Trung Quy Phan , Palaiahnakote Shivakumara Tong Lu and Chew Lim Tan. Introduction. Text extraction from video frames  video search and retrieval. Introduction. Low resolution Complex background Unconstrained appearance.

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Recognition of Video Text Through Temporal Integration

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  1. Recognition of Video TextThrough Temporal Integration TrungQuyPhan, PalaiahnakoteShivakumaraTong Lu and Chew Lim Tan

  2. Introduction • Text extraction from video frames video search and retrieval

  3. Introduction • Low resolution • Complex background • Unconstrained appearance

  4. Introduction • Low resolution • Complex background • Unconstrained appearance • Temporal information

  5. Problem • Input • Word bounding box in a reference frame • Frame ID • Output • Binarized image • Scope • Static texts • Linearly moving texts

  6. Approach • Tracking • Alignment • Integration • Refinement

  7. 1. Tracking • Find • [tstart, tend]  text framespan • Bounding box in each frame  text instance tstart … … tend tref

  8. 1. Tracking • Text descriptors

  9. 1. Tracking • Text descriptors • Stroke Width Transform-SIFT

  10. 1. Tracking • t = tref + 1, tref + 2, … • Initialize search area

  11. 1. Tracking • t = tref + 1, tref + 2, … • Initialize search area • If matchRatio ≥ 0.1  estimate new BB

  12. 1. Tracking • t = tref + 1, tref + 2, … • Initialize search area • If matchRatio ≥ 0.1  estimate new BB • Otherwise, found tend

  13. 2. Alignment • Align at pixel-level  better integration

  14. 2. Alignment • Align at pixel-level  better integration • Slide reference text mask over individual masks  optimal alignment

  15. 2. Alignment • Align at pixel-level  better integration • Slide reference text mask over individual masks  optimal alignment

  16. 3. Integration • Text probability map

  17. 3. Integration • Initial binarization

  18. 4. Refinement • SWT: rounded strokes • Intensity values preserve sharp edges & holes suppress background pixels

  19. Experiments • Moving text dataset: English + German • 250 words • 1,545 characters • Bottom to top, right to left and left to right • Static text dataset: English • 212 words • 1,389 characters

  20. Experiments • Methods for comparison • Niblack (Single) • Min/max (Multiple) • Average-Min/max (Multiple) • Ours (Single) • Ours (Multiple)

  21. Sample Results

  22. Sample Results

  23. Results on Moving Texts • Character recognition rate (CRR) • Word recognition rate (WRR)

  24. Results on Static Texts • Multiple-frame: ~20% improvement over single-frame

  25. Summary • A variation of SIFTfor robust tracking • Integration based onword masks • Future work: handle complex text movements

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