<title>Word-level recognition of multifont Arabic text using a feature vector matching approach</title>

Abstract

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters.This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition.A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream.Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.

Printed Arabic Script Recognition: A Survey

“…Segmentation-free or holistic Arabic OCR systems perform the recognition on the entire word as a unit without segmenting the word or recognizing characters separately [2]. Several studies have investigated the holistic approach for printed Arabic scrip OCR such as in [79], [16], [8]. OCR systems based on a holistic approach require tracing the feature of the entire word and dealing with words instead of characters.…”

Printed Persian Subword Recognition Using Wavelet Packet Descriptors

“…There are many works reported on the recognition of Arabic and Farsi texts [1,[10][11][12][13][14][15][16][17][18][19][20][21][22][23]. There are few works based on holistic recognition of Farsi/Arabic subwords by their shape information.…”

Farsi document image recognition system using word layout signature

“…Here, the upper contours of words are extracted and then a picture dictionary of these features is made, and each subword is shown as a combination of contour strokes that includes upper, lower, and middle positions of the baseline. As another example, the work proposed in [23] depends on the feature of the shape of printed words in the recognition of Arabic texts written in three different fonts, two of which are synthetic. Several features such as dots, directional segments, directional cavities, junctions and endpoints, connectors, inner word spaces, and descenders of the Arabic printed words are extracted and saved in a dictionary.…”