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Iyad Abu Doush is an Associate Professor in the department of Computer Science and Information Systems at American University of Kuwait.
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He became the chairman of Computer Sciences department at Yarmouk University in September 2010. He is interested in knowledge-based systems, knowledge representation and reasoning, intelligent systems, constraint satisfaction and optimization problems. from Grenoble 1 university (2008), M.Sc from Grenoble 1 university (2004), M.Sc from Yarmouk University (2003), and his B.Sc. The article is published in the original.įaisal Alkhateeb is an Associate Professor in the department of Computer Sciences at Yarmouk University. on Image Analysis and Recognition (Vilamoura, 2014), pp.
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Al Azawi et al., “Character-level alignment using WFST and LSTM for post-processing in multi-script recognition systems-A comparative study,” in Proc. Workshop on Document Analysis Systems (Tours, 2014), pp. Breuel, “Context-dependent confusions rules for building error model using weighted finite state transducers for OCR post-processing,” in Proc. on Writing and Document CIFED’02 (Hammamet, 2002), pp. Pechwitz et al., “IFN/ENIT-database of handwritten Arabic words,” in Proc.
#Ocr arabic text recognition software how to
Al Raoof Bsoul, “What we have and what is needed, how to evaluate Arabic Speech Synthesizer?,” Int. Al Raoof Bsoul, “AraDaisy: A system for automatic generation of Arabic DAISY books,” Int. on Document Analysis and Recognition ICDAR’09 (Barcelona, 2009), pp. Slimane et al., “A new arabic printed text image database and evaluation protocols,” in Proc. Sakhr: paired model evaluation of two Arabic OCR products,” Proc. on Document Analysis and Recognition (ICDAR) (Beijing, 2011), pp. Slimane et al., “ICDAR 2011-arabic recognition competition: multi-font multi-size digitally represented text,” in Proc. on Image Analysis and Processing (Springer, 2013), pp. Jaiem et al., “Database for Arabic printed text recognition research,” in Proc. on Document Analysis and Recognition (Washington, 2013), pp. Krayem, et al., “Holistic Arabic whole word recognition using HMM and block-based DCT,” in Proc. on Document Analysis and Recognition (Montreal, 1995), Vol. Haralick, “Segmentation-free word recognition with application to Arabic,” in Proc. Bergmann, “An Arabic optical character recognition system using recognition-based segmentation,” Pattern Recogn. Makhoul, “An omnifont open-vocabulary OCR system for English and Arabic,” IEEE Trans. Hassin et al., “Printed arabic character recognition using HMM,” J. on Document Analysis and Recognition (ICDAR) (Tunis, 2015), pp.
#Ocr arabic text recognition software tv
Garcia, “ALIF: A dataset for arabic embedded text recognition in TV broadcast,” in Proc. on Document Analysis and Recognition (Beijing, 2011), pp. Breuel, “An evaluation of HMM-based techniques for the recognition of screen rendered text,” in Proc.
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Sakhr Software Arabic Language Technology. com/help/leadtools/v15/ocr/api/whnjs.htm.
#Ocr arabic text recognition software Pc
Mendelson, “ABBYY finereader professional 9.0,” PC Mag. on Computer Systems and Applications AICCSA’07 (Amman, 2007), pp. Nourani, “Handwritten farsi/arabic word recognition,” in Proc. Omar, “Methods of arabic language baseline detectionthe state of art,” Int. Belghith, “Towards a distributed arabic OCR based on the DTW algorithm: Performance analysis,” Int. on Information Technology: New Generations (Las Vegas, 2008), pp.
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AlKhateeb et al., “Knowledge-based baseline detection and optimal thresholding for words segmentation in efficient preprocessing of handwritten Arabic text,” in Proc. Chikhi, “Combining neural networks for arabic handwriting recognition,” Int. on Programming and Systems (ISPS) (Algiers, 2011), pp. Chikhi, “Combining neural networks for arabic handwriting recognition,” in Proc. on Language Engineering (Cairo, 2006), pp. Abdelazim, “Recent trends in arabic character recognition,” in Proc. Ahmad, “ASCII based GUI system for arabic scripted languages: a case of urdu,” Int. Comrie, The World’s Major Languages (Routledge, 2009).ī. Line Eikvil, Optical character recognition (1993). on Document Analysis and Recognition ICDAR (2007), Vol. Zahour et al., “Text line segmentation of historical arabic documents,” in Proc. on Information and Communication Technology (ICICT) (Karachi, 2005), pp. Zeki, “The segmentation problem on arabic character recognition the state of the art,” in Proc. Govindaraju, “Offline arabic handwriting recognition: a survey,” IEEE Trans. Ahmed, “Optical character recognition system for arabic text using curstional approach,” J.