14. A text detection process, comprising: preprocessing an image by segmentation using statistical region merging, removing regions that are definitely not text and grouping regions based on criteria of height similarity, color similarity, region distance and horizontal alignment defined as follows: height similarity is defined as max ⁡ ( HEIGHT 1 , HEIGHT 2 ) min ⁡ ( HEIGHT 1 , HEIGHT 2 ) < T h ⁢ ⁢ e ⁢ ⁢ i ⁢ ⁢ g ⁢ ⁢ h ⁢ ⁢ t ⁢ ⁢ _ ⁢ ⁢ s ⁢ ⁢ i ⁢ ⁢ m , where HEIGHT 1 and HEIGHT 2 are heights of two regions and min(HEIGHT 1 ,HEIGHT 2 ) is a non-zero number; 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; region distance is defined as D region <T region , where D region is the horizontal distance of the two regions, and horizontal alignment is defined as D top <T align or D bottom <T align , where D top and D bottom are vertical distances between a top boundary and a bottom boundary; carrying out a feature extraction process to describe each remaining region, where each feature is represented by a stroke feature, an edge feature and a fill factor feature of the region; and classifying each feature by use of a support vector machine (SVM) classifier engine which outputs whether the region is text or not using the following equation: sgn ⁡ ( ∑ i = 1 l ⁢ y i ⁢ α i ⁢ K ⁡ ( x i , x ) + b ) , where (x i , y i ) are feature vectors and groundtruth labels of training samples, x are feature vectors of the regions to be classified, α i and b are parameters obtained by solving the optimization problem defined as min α ⁢ 1 2 ⁢ α T ⁢ Q ⁢ ⁢ α - ⅇ T ⁢ α and subject to y T α=0 (0≦α i ≦C, i=1, . . . , l), and K is defined as K ⁡ ( X , X j ) = exp ⁢ { -  X - X j  2 2 ⁢ σ 2 } where 2σ 2 is a non-zero number to obtain a classification output where 1 indicates the presence of text and −1 indicates the absence of text.
14. A text detection process, comprising: preprocessing an image by segmentation using statistical region merging, removing regions that are definitely not text and grouping regions based on criteria of height similarity, color similarity, region distance and horizontal alignment defined as follows: height similarity is defined as max ⁡ ( HEIGHT 1 , HEIGHT 2 ) min ⁡ ( HEIGHT 1 , HEIGHT 2 ) < T h ⁢ ⁢ e ⁢ ⁢ i ⁢ ⁢ g ⁢ ⁢ h ⁢ ⁢ t ⁢ ⁢ _ ⁢ ⁢ s ⁢ ⁢ i ⁢ ⁢ m , where HEIGHT 1 and HEIGHT 2 are heights of two regions and min(HEIGHT 1 ,HEIGHT 2 ) is a non-zero number; 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; region distance is defined as D region <T region , where D region is the horizontal distance of the two regions, and horizontal alignment is defined as D top <T align or D bottom <T align , where D top and D bottom are vertical distances between a top boundary and a bottom boundary; carrying out a feature extraction process to describe each remaining region, where each feature is represented by a stroke feature, an edge feature and a fill factor feature of the region; and classifying each feature by use of a support vector machine (SVM) classifier engine which outputs whether the region is text or not using the following equation: sgn ⁡ ( ∑ i = 1 l ⁢ y i ⁢ α i ⁢ K ⁡ ( x i , x ) + b ) , where (x i , y i ) are feature vectors and groundtruth labels of training samples, x are feature vectors of the regions to be classified, α i and b are parameters obtained by solving the optimization problem defined as min α ⁢ 1 2 ⁢ α T ⁢ Q ⁢ ⁢ α - ⅇ T ⁢ α and subject to y T α=0 (0≦α i ≦C, i=1, . . . , l), and K is defined as K ⁡ ( X , X j ) = exp ⁢ { -  X - X j  2 2 ⁢ σ 2 } where 2σ 2 is a non-zero number to obtain a classification output where 1 indicates the presence of text and −1 indicates the absence of text. 17. The method according to claim 14 , where the binarization is carried out using a plurality of binarization methods with each binarized output being processing by an optical character reader to produce multiple outputs that are combined.