Character Recognition by Minoru Mori (Editor)

By Minoru Mori (Editor)

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The preposition “to” in image (c) is recognized correctly. In case of small words, the pre-processing clearly improves the performance due to the filtering algorithm in the pre-processing. (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Fig. 4. (a) – (j) Represents different images under consideration. Image Output of GOCR Output of Tesseract Sta_ring Govind_. Reem_ _n. RajpaI Yadav. Om Puri. Starring Guvinda, Rcema Sen, Raipal Yadav, Om Puri. (c) _____ ___ ___ _________ ____ __ __ SmS YR SH to Pluww SMS thu fnlluwmg (adn In 56633 SMS YR SH in 56633 After Applying Proposed Preprocessing Algorithms GOCR Tesseract Starring Govind_.

The pseudo code for removing the non textual part is as below:  Run connected component analysis for all Pc  Vmorph into n number of components ( ci ) where Pc to split the candidate pixels is a pixel in Vmorph ci  Find the area for each  Remove the components with area smaller or greater than two experimentally obtained threshold values. 7 Confirmation of the Text regions using Temporal Consistency This portion of the proposed method is based on the observation that texts of Breaking news persists in the video for at least 1-2 seconds.

Shivakumara, ICPR 2008) have used edge based features and heuristic based filters to localize text regions and applied some geometry based heuristics to reduce the false positives. Combined feature based approach: (Y. Song, 2009) have used degree of texture roughdetail to localize the candidate text regions and subsequently used Sobel edge operator to minimize the false positives. (S. Yuting, 2008) have used both texture and edge based approach to localize the candidate text regions and then they have applied SVM to segregate background from foreground.

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