Open Access Open Access  Restricted Access Subscription or Fee Access

Automatic Computer Vision System to Help the Blind or Visually Impaired by Converting the Written Arabic Text to Audible Style

Mokhtar H. Mohamed


The goal of this research is to design and implement a portable computerized system that helps the blind or visually impaired to hear Arabic texts which are printed or written by free hand. The system captures an image of Arabic text as an input. The complexity of Arabic language makes the research in recognition of printed and handwritten Arabic text to be untouched for many years compared with other languages such as English and Chinese. In the last years a significant number of researches has been carried out to recognizing printed and handwritten. However, Arabic characters recognition is still an open research field due to its cursive nature

The proposed system first aligns the taken image to a prototype template. Then, it subtracts the background. Foreground image will be segmented into lines. Each line is segmented into isolated words. The word will be segmented to sub-words. The word or sub-word will be segmented into isolated characters. Finally, the features of each segmented character are calculated in features vector. This vector will be compared with a pre-defined templates of feature vectors for all of the Arabic letters. The last step is to pass the segmented word to a program that synthesized and pronounce this word. The proposed framework has been evaluated using over dozen thousands of different Arabic words. The experimental results show that average recognition efficiency of the proposed framework is highly significant compared with other methods.


Correlation, Fuzzy C-Mean, Image Alignment, Image Retrieval, OCR, Text to Speech Algorithms.

Full Text:



Mohamed Ben Halima, Hichem karray, Adel. M. Alimi, and Ana Fernández Vila. “NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR”, International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 3, No. 10, (2012).

Atallah AL-Shatnawi and Khairuddin Omar, “Methods of Arabic Language Baseline Detection – The State of Art”, IJCSNS International Journal of Computer Science and Network Security (IJCSNS), VOL.8 No.10, October (2008).

A. Cheung, M. Bennamoun, N.W. Bergmann,” An Arabic optical character recognition system using recognition-based segmentation” Pattern Recognition 34 ,233, (2001).

A. Amin, O!-line Arabic character recognition: the state of art, Pattern Recognition 31 (5) 517,530, (1998).

A. Cheung, M. Bennamoun, N.W. Bergmann, A new word segmentation algorithm for Arabic script, DICTA: Digital Imaging Comput. Tech. Appl. 431-435, (1997).

B. Al-Badr, R.M. Haralick, Segmentation free word recognition with application to Arabic, ICDAR: Third International Conference on Document Analysis and Recognition, 1995.

H. Al-Youse", S.S. Udpa, Recognition of Arabic character, IEEE Trans. Pattern. Anal. Mach. Intell. 14 (8) 853-857, (1992).

Abdel wadood Mesleh1, Ahmed Sharadqh, Jamil Al-Azzeh, Mazen Abu-Zaher, Nawal Al-Zabin, Tasneem Jaber, Aroob Odeh and Myssa'a Hasn, “An Optical Character Recognition”, Contemporary Engineering Sciences, Vol. 5, no. 11, 521 – 529, (2012).

H. Al-Muhtaseb and R. Qahwaji, Arabic Optical Character Recognition: Recent Trends and Future Directions, Applied Signal and Image Processing: Multidisciplinary Advancements, ed. R. Qahwaji, R. Green and E. Hines, 324-346, 2011.

Bihina, M. M. B. The Phone Reader. Grahams town, South Africa, November, 2012.

Anusha A. Pingale , Devshree D. Mistry , Shital R. Kotkar , Hemangi A. Pachpande “Product Reading for Visually Impaired Persons ”, International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 04 | July-2015 , 1688 : 1699

Rupali and Dharmale (2015), „Text Detection and Recognition with Speech Output for Visually Challenged Person‟, Research Gate- International Journal of Engineering Research and Applications, Vol. 5, Issue. 3, pp.84-87.

Xiaodong Yang, Yingli Tian Chucai Yi Aries Arditi “Context-based Indoor Object Detection as an Aid to Blind Persons Accessing Unfamiliar Environments” 2010S.

Sneha Sharma, Dr. Roxanne Canosa, advisor “Extraction of Text Regions in Natural Images” 2007

Yingli Tian, Chucai Yi “Assistive Text Reading from Complex Background for Blind Persons”.

M. Rashad and N. A. Semary, “Isolated Printed Arabic Character Recognition Using KNN and Random Forest Tree Classifiers,” Cham: Springer International Publishing, pp. 11–17, 2014. DOI: 10.1007/9783-319-13461-1_2

L. Chergui and M. Kef, A Serial Combination of Neural Network for Arabic OCR. Cham: Springer International Publishing, pp. 297–303, 2014. DOI: 10.1007/978-3-319-07998-1_34

M. Amara, K. Zidi, S. Zidi, and K. Ghedira, “Arabic Character Recognition Based M-SVM: Review,” Cham: Springer International Publishing, pp. 18–25, 2014. DOI: 10.1007/978-3-319-13461-1_3

Z. Jiang, X. Ding, L. Peng, and C. Liu, “Modified Bootstrap Approach with State Number Optimization for Hidden Markov Model Estimation in Small-Size Printed Arabic Text Line Recognition,” Cham: Springer International Publishing, pp. 437–441, 2014. DOI: 10.1007/978-3-31908979-9_33

S. B. Ahmed, S. Naz, M. I. Razzak, S. F. Rashid, M. Z. Afzal, and T. M. Breuel, “Evaluation of cursive and non-cursive scripts using recurrent neural networks,” Neural Computing and Applications, vol. 27, no. 3, pp. 603–613, 2016. DOI: 10.1007/s00521-015-1881-4

A. Z. Muhammad Sarfraz and S. N. Nawaz, “Computer-Aided Intelligent Recognition Techniques and Applications,” John Wiley & Sons, Ltd, ch. On Offline Arabic Character Recognition, pp. 1–18, May 2005.

M. N. Abdi and M. Khemakhem, “A model-based approach to offline text-independent arabic writer identification and verification,” Pattern Recognition, vol. 48, no. 5, pp. 1890 – 1903, 2015.

Raghu Krishnapuram, Swarup Medasani, Sung-Hwan Jung, Young-Sik Choi, and Rajesh Balasubramaniam, " Content-Based Image Retrieval Based on a Fuzzy Approach", EEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 16, NO. 10, OCTOBER 2004.

R. Krishnapuram and R. Medasani, “A Fuzzy Approach to Graph Matching,” Proc. IFSA Congress Conf., pp. 1029-1033, Aug. 1999.

S. Medasani, R. Krishnapuram, and Y. Choi, “Graph Matching by Relaxation of Fuzzy Assignments,” IEEE Trans. Fuzzy Systems, vol. 9, no. 1, pp. 173-182, 2001.

S. Gold and A. Rangarajan, “A Graduated Assignment Algorithm for Graph Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 377-387, Apr. 1996.

M.P. Windham, “Numerical Classification of Proximity Data with Assignment Measure,” J. Classification, vol. 2, pp. 157-172, 1985.

Mithe, R., Indalkar S., Divekar, N. Optical Character Recognition. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-1, March 2013.

Ashraf A. Shahin," Printed Arabic Text Recognition using Linear and Nonlinear Regression ", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 1, 2017.

K. Zagoris, I. Pratikakis, A. Antonacopoulos, B. Gatos, and N. Papamarkos, “Distinction between handwritten and machine-printed text based on the bag of visual words model,” Pattern Recognition, vol. 47, no. 3, pp. 1051 – 1062, 2014.


  • There are currently no refbacks.