1. A method for automatically recognizing Arabic text, comprising: acquiring a text image comprising one or more Arabic words each including one or more Arabic characters; identify a plurality of lines of Arabic text in the text image; segmenting one of the plurality of lines of Arabic text into Arabic words; digitizing at least one of the Arabic words to form a two-dimensional array of pixels each associated with a pixel value, wherein the pixel value is expressed in a binary number; dividing the one of the Arabic words into a plurality of line images; defining a plurality of cells in one of the plurality of line images, wherein each of the plurality of cells comprises a group of adjacent pixels; serializing pixel values of pixels in each of the plurality of cells in one of the plurality of line images to form a binary cell number; forming a text feature vector according to binary cell numbers obtained from the plurality of cells in one of the plurality of line images; and feeding the text feature vector into a Hidden Markov Model to recognize the one or more Arabic words including the Arabic characters.
3. The method of claim 1 , further comprising: building an Arabic corpus comprising Arabic text files and ground truths corresponding to each of the Arabic text files, wherein the Arabic text files include Arabic texts written in different writing styles; and storing writing-style indices in association with the Arabic text files by a computer, wherein each of the writing-style indices indicates that one of the Arabic text files is written in one of the writing styles, wherein the Hidden Markov Model is trained using the Arabic text files and ground truths in the Arabic corpus in accordance with the writing-style indices in association with the Arabic text files.