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. 10. The method according to claim 1 , where edge features are measurement of a smoothness of edges, uniformity of edges and amount of edges in a candidate region, where the smoothness of edges is represented by a percentage of neighborhoods that have the same direction, uniformity of edges is calculated as a frequency of an edge direction that appears most often, and an amount of edges is measured by a ratio of a length of total edges to area of the candidate region.