Segmenting Arabic Handwritten Documents into Text lines and Words

In this paper, we present a method for segmenting Arabic handwritten documents into text lines and words. Text line segmentation is addressed by a well-known technique, the horizontal projection profile, in which autocorrelation is used to enhance the self similarity of this profile. This technique promotes the estimation of text line spacing. Word extraction is based on an adaptation of a known method, gap metrics.This improvement relies on deriving the values of these gaps from the properties of each input document, making the proposed method tolerant and robust to Arabic handwritten nature. Text is often divided into words, sub-words and letters; however, some letters do not connect to the following letter, even in the middle of a word. A gap metric method exploits the membership values of a clustering algorithm to identify segmentation thresholds as “within word” or “between words” gaps. The proposed method is tested on the benchmarking datasets of Arabic handwritten text recognition research (AHDB), and very promising results were achieved, with an 84.8% correct extraction rate.