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.
7. The method according to claim 1 , where the stroke width features comprise a feature value representing a percentage of rows whose standard deviation of horizontal stroke width is within a threshold, or that are clustered into groups and standard deviation of horizontal stroke width in each group is within a threshold, or the percentage of rows having similar stroke widths or clusters of similar stroke widths.