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Arabic Handwritten Documents Segmentation into Text-lines and Words using Deep Learning
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In: ASAR ; https://hal.inria.fr/hal-02460880 ; ASAR, Sep 2019, Sydney, Australia (2019)
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Line and Word Segmentation of Arabic handwritten documents using Neural Networks ; Segmentation en lignes et en mots de documents arabes manuscrits utilisant des modèles neuronnaux
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In: https://hal.inria.fr/hal-01910559 ; [Research Report] LORIA - Université de Lorraine; READ. 2018 (2018)
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Named Entity Recognition by Neural Prediction
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In: International Conference on Image Processing, Computer Vision, & Pattern Recognition ; https://hal.inria.fr/hal-01981613 ; International Conference on Image Processing, Computer Vision, & Pattern Recognition, Jul 2018, Las Vegas, United States (2018)
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Impact of Features and Classifiers Combinations on the Performances of Arabic Recognition Systems
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In: International Workshop on Arabic Script Analysis and Recognition ; https://hal.inria.fr/hal-01981528 ; International Workshop on Arabic Script Analysis and Recognition, Apr 2017, NANCY, France (2017)
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Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs
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In: ICDAR 2015 - 13th International Conference on Document Analysis and Recognition ; https://hal.inria.fr/hal-01254724 ; ICDAR 2015 - 13th International Conference on Document Analysis and Recognition, Aug 2015, Nancy, France. pp.51 - 55, ⟨10.1109/ICDAR.2015.7333724⟩ (2015)
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A Neural-Linguistic Approach for the Recognition of a Wide Arabic Word Lexicon
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In: Document Recognition and Retrieval XVII ; https://hal.inria.fr/inria-00579680 ; Document Recognition and Retrieval XVII, Jan 2010, San Jose, United States (2010)
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Automation of Indian Postal Documents written in Bangla and English
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In: ISSN: 0218-0014 ; EISSN: 0218-0014 ; International Journal of Pattern Recognition and Artificial Intelligence ; https://hal.inria.fr/inria-00435501 ; International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing, 2009, 23 (8), pp.1599-1632. ⟨10.1142/S0218001409007776⟩ (2009)
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Abstract:
International audience ; In this paper, we present a system towards Indian postal automation based on pin-code and city name recognition. Here, at first, using Run Length Smoothing Approach (RLSA), non-text blocks (postal stamp, postal seal, etc.) are detected and using positional information Destination Address Block (DAB) is identified from postal documents. Next, lines and words of the DAB are segmented. In India, the address part of a postal document may be written by combination of two scripts: Latin (English) and a local (State/region) script. It is very difficult to identify the script by which pin-code part is written. To overcome this problem on pin-code part, we have used two-stage artificial neural network based general scheme to recognize pin-code numbers written in any of the two scripts. To identify the script by which a word/city name is written, we propose a water reservoir concept based feature. For recognition of city names, we propose an NSHP-HMM (Non- Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the proposed digit numeral recognition module is 93.14% while that of city name recognition scheme is 86.44%.
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Keyword:
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
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URL: https://hal.inria.fr/inria-00435501/document
https://doi.org/10.1142/S0218001409007776
https://hal.inria.fr/inria-00435501
https://hal.inria.fr/inria-00435501/file/ijprai-final-12-11-08.pdf
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| 10 |
Segmentation of Continuous Document Flow by a modified Backward- Forward algorithm
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In: SPIE - Electronic Imaging ; https://hal.inria.fr/inria-00347217 ; SPIE - Electronic Imaging, 2009, Los Angeles, United States (2009)
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| 11 |
A neural perceptive model for the recognition of a large canonical Arabic word vocabulary
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In: International Arab Conference on Information Technology ; https://hal.inria.fr/inria-00600294 ; International Arab Conference on Information Technology, Dec 2009, Sana'a, Yemen (2009)
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HMM and fuzzy logic: A hybrid approach for online Urdu script-based languages' character recognition
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In: ISSN: 0950-7051 ; EISSN: 1872-7409 ; Knowledge-Based Systems ; https://hal.inria.fr/inria-00579697 ; Knowledge-Based Systems, Elsevier, 2009, 23 (8), pp.914-923. ⟨10.1016/j.knosys.2010.06.007⟩ (2009)
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Effect of Ghost Character Theory on Arabic Script Based Languages Character Recognition
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In: WASE Global Conference on Image Processing and Analysis - GCIA09 ; https://hal.inria.fr/inria-00579666 ; WASE Global Conference on Image Processing and Analysis - GCIA09, Feb 2009, Taiwan, China (2009)
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Arabic natural language processing: handwriting recognition
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In: International Arab Conference on Information Technology - ACIT'2008 ; https://hal.inria.fr/inria-00347229 ; International Arab Conference on Information Technology - ACIT'2008, Dec 2008, Hammamet, Tunisia (2008)
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A Novel Approach for the Recognition of a wide Arabic Handwritten Word Lexicon
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In: International Conference on Document Analysis and Recognition ; https://hal.archives-ouvertes.fr/hal-00347179 ; International Conference on Document Analysis and Recognition, Dec 2008, Tampa, United States. pp.ThAT6.5 (2008)
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Cursive Bengali Script Recognition for Indian Postal Automation ; Reconnaissance de l'écriture manuscrite cursive Bengali pour l'automatisation de la Poste Indienne
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A System for Indian Postal Automation
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In: International Conference on Document Analysis and Recognition ; https://hal.inria.fr/inria-00000364 ; International Conference on Document Analysis and Recognition, Sep 2005, Seoul, Korea (2005)
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A System for Indian Postal Automation
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In: International Workshop on Document Analysis ; https://hal.inria.fr/inria-00000103 ; International Workshop on Document Analysis, Umapada Pal, Mar 2005, Kolkata, India (2005)
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Du manuscrit à l'impression sans saisie
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In: Colloque sur la vision artificielle - CVA'00 ; https://hal.inria.fr/inria-00099150 ; Colloque sur la vision artificielle - CVA'00, 2000, Tizi Ouzou, Algérie, 49 p (2000)
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