1. A method of text detection in a video image, comprising: at an image processor, receiving a video frame that potentially contains text; segmenting the video frame into regions having similar color; where color similarity is defined as 
 D ( c 1 ,c 2 )=√{square root over (( R 1 − R 2 ) 2 +( G 1 − G 2 ) 2 +( B 1 − B 2 ) 2 )}< T color , where c 1 =[ R 1 G 1 B 1 ] and c 2 =[ R 2 G 2 B 2 ] are average colors of two regions and T color is a merging threshold; identifying high likelihood non-text regions from the regions having similar color and discarding the high likelihood non-text regions; merging regions having similar color and having horizontal positions that are within a threshold; describing the regions using features by carrying out a feature extraction process to extract stroke features, edge features, and fill factor features; and passing the remaining regions through a trained binary classifier to obtain final text regions which are binarized for processing by OCR software.
1. A method of text detection in a video image, comprising: at an image processor, receiving a video frame that potentially contains text; segmenting the video frame into regions having similar color; where color similarity is defined as 
 D ( c 1 ,c 2 )=√{square root over (( R 1 − R 2 ) 2 +( G 1 − G 2 ) 2 +( B 1 − B 2 ) 2 )}< T color , where c 1 =[ R 1 G 1 B 1 ] and c 2 =[ R 2 G 2 B 2 ] are average colors of two regions and T color is a merging threshold; identifying high likelihood non-text regions from the regions having similar color and discarding the high likelihood non-text regions; merging regions having similar color and having horizontal positions that are within a threshold; describing the regions using features by carrying out a feature extraction process to extract stroke features, edge features, and fill factor features; and passing the remaining regions through a trained binary classifier to obtain final text regions which are binarized for processing by OCR software. 12. The method according to claim 1 , where regions of high likely-hood of being non-text are decided by the following: (1) if region_height is smaller than some threshold T_low, or the region_height is larger than some threshold T_high, or (2) if region_area is smaller than some threshold T_area, or (3) if the region touches one of the four sides of the image border, and its height is larger than a threshold T, or (4) if a fill_factor defined as fill_factor = Region ⁢ ⁢ Area Bounding ⁢ ⁢ Box ⁢ ⁢ Area , where Bounding Box Area is a non-zero number, is lower than a threshold, then a region is considered to be a high likelihood non-text region.