Printed Arabic optical character segmentation

Abstract

A considerable progress in recognition techniques for many non-Arabic characters has been achieved. In contrary, few efforts have been put on the research of Arabic characters. In any Optical Character Recognition (OCR) system the segmentation step is usually the essential stage in which an extensive portion of processing is devoted and a considerable share of recognition errors is attributed. In this research, a novel segmentation approach for machine Arabic printed text with diacritics is proposed. The proposed method reduces computation, errors, gives a clear description for the sub-word and has advantages over using the skeleton approach in which the data and information of the character can be lost. Both of initial evaluation and testing of the proposed method have been developed using MATLAB and shows 98.7% promising results.

A Review of Arabic Optical Character Recognition Techniques & Performance

“…Text recognition can generally be divided into two categories; Online and Offline [11,12]. In online recognition, the characters/glyph recognized while the user writes the text -usually on a digitized pen tracer with a special stylus pen [13,14].…”

“…4. Classification: comparing the unknown images of symbols (feature extraction) with predefined stored samples in order to identify their type [2], its determines the region of feature space in which an unknown pattern falls [11].…”

“…5. Post-processing: this is the final stage in OCR system, and the most important stage [11]. Post-processing stage is working for checking the result text from previous stage, and correct it to make sure it is free from errors [12].…”

Balochi Non Cursive Isolated Character Recognition using Deep Neural Network

“…They also discussed the representation of Arabic letters, words, and analyzed handwritten methods. Mohammad et al [4] also explored a segmentation and recognition technique for Arabic text and using a contour-based approach that found out edges and the region of interest for the sub-words and claimed improvements over the finding of the skeleton of the word. Solimanpour et al [5] explored the contour-based method for the recognition of Farsi character recognition.…”

A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network

“…Arabic is a challenging task due to several reasons [16], [17]. In character segmentation, the main problem is over segmentation of the characters like Seen ‫,)ﺱ(‬ Lam-Alif ‫,)ﻻ(‬ Sad ‫)ﺹ(‬ and etc., segmented into more than one stage (parts) of the complete recognition system [4], [10]- [19]. The most optimal available methods applicable for over-segmentation is the one introduced by [18].…”