1. A computer-implemented method for automatically recognizing Arabic text, comprising: acquiring a text image containing a line of Arabic characters; digitizing the line of the Arabic characters to form a two-dimensional array of pixels each associated with a pixel value expressed in a binary number, wherein the two-dimensional array of pixels comprises a plurality of rows in a first direction and a plurality of columns in a second direction; counting frequencies of consecutive pixels of a same pixel value in a string of pixels in a column of pixels, wherein adjacent strings of pixels each having different pixel values is defined by a transition in between, wherein the step of counting further comprises: stopping counting frequencies of consecutive pixels of a same pixel value in a column when number of transitions in the column reaches a predetermined cut-off transition number, wherein the predetermined cut-off transition number is obtained by statistical analysis on Arabic text prior to the step of digitizing the line of the Arabic characters; forming a text feature vector using the frequency counts obtained from the strings in the column of pixels; and feeding the text feature vector into a Hidden Markov Model to recognize the line of Arabic characters.
1. A computer-implemented method for automatically recognizing Arabic text, comprising: acquiring a text image containing a line of Arabic characters; digitizing the line of the Arabic characters to form a two-dimensional array of pixels each associated with a pixel value expressed in a binary number, wherein the two-dimensional array of pixels comprises a plurality of rows in a first direction and a plurality of columns in a second direction; counting frequencies of consecutive pixels of a same pixel value in a string of pixels in a column of pixels, wherein adjacent strings of pixels each having different pixel values is defined by a transition in between, wherein the step of counting further comprises: stopping counting frequencies of consecutive pixels of a same pixel value in a column when number of transitions in the column reaches a predetermined cut-off transition number, wherein the predetermined cut-off transition number is obtained by statistical analysis on Arabic text prior to the step of digitizing the line of the Arabic characters; forming a text feature vector using the frequency counts obtained from the strings in the column of pixels; and feeding the text feature vector into a Hidden Markov Model to recognize the line of Arabic characters. 5. The computer-implemented method of claim 1 , wherein the pixel values in the two-dimensional array are expressed in single-bit binary numbers.