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Register a new userArabic Handwritten Word Recognition using CBIR
تعرف الكلمة العربية المكتوبة بخط اليد باستخدام استرجاع الصور اعتماداً على المحتوى
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Aِl-Baath University ورقة بحثية
Publication date
2017
fields
Informatics Engineering
and research's language is
العربية
Created by
Shamra Editor
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يصف هذا البحث نظاما للتعرف على الكلمة العربية المكتوبة بخط اليد دون تقطيع سابق للكلمة إلى محارف, و سيتم التعرف من خلاله على مستويين, و قد طور هذا النظام اعتمادا على نظام التعرف الضوئي على الحروف, و خوارزمية ماركوف المخفية, و عدد من التوابع (المورفولوجية), و خوارزميات استرجاع الصور اعتمادا على المحتوى.
This research describes a system for recognition of handwritten Arabic word without prior segmentation of the word into characters. In this system, the recognition will be happened at two levels. It is evolved basing on OCR (Optical Character Recognition), Hidden Markov Model, CBIR(Content Based Image Retrieval), it also involves Mathematical Morphology.
References used
KURDY, M. B. 1993- Multifont Arabic/Latin Optical Character Recognition System, Congres international Langue arabe et technologies information avancees , December, 1993
VASELINA, J. 2006 -A bout some Application of Hidden Markov Model in intrusion detection system , international conference and computer systems and technologies, compsys tech 6
CONZALEZ R. ; WOODS, R. 2008 – Digital Image Processing. Person Education , 3d Edition
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Designing Computerized Systems which posses reading and hearing
faculties is an active research area for more than four decades. Many
methods and algorithms have been suggested by researches for this
purpose as part of pattern recognition research
. Recently, more
research work has been devoted to the holist approach the
recognition system recognizes a complete word as one object without
going through the long and erroneous character segmentation
process. In this paper, a convolutional neural network has been
designed to recognize the popular Arabic names holistically. SUSt
ARG names data set has been used to test the network performance
(collected and compiled by pattern recognition research in Sudan
University of Science and Technology-SUSt). Selecting an appropriate
deep learning toolbox, after five stages of training, the network was
able to recognize all the names and 100%
هدفنا من خلال هذه الدراسة في إطار المشروع الفصلي للسنة الرابعة إلى إلقاء الضوء على استرجاع الصور من مجموعة كبيرة بالاعتماد على محتوى صورة هدف , و قمنا بتدعيم هذه الدراسة بتطبيق ضمن بيئة الماتلاب لبرنامج بحث عن الصور المشابهة لصورة مدخلة .
و قد تركز
بحثنا على ميزتين هامتين يكاد لا يخلو منها أي نظام بحث عن الصور بالاعتماد على المحتوى و هما ميزتي الهيستوغرام اللوني و بنية الصورة texture , ووضحنا الخطوات التي يتم في ضوئها عملية الاسترجاع بدءاً من تحليل الصورة و استخلاص شعاع الواصفات الخاص فيها , و مطابقته مع أشعة الميزات الخاصة بالصور الموجودة في قاعدة البيانات ليتم ترتيب الصور بحسب مدى تشابهها من الصورة الهدف .
و تطرقت الدراسة إلى استخدام الفضاء اللوني HMMD كبديل للفضاء اللوني RGB لاستخراج واصفات البنية اللونية على اعتبار أنه نموذج لوني موجه بالمستخدم user oriented و بالتالي نضمن أن نحصل على نتائج أفضل ترضي المستخدم .
وقمنا بتدعيم الدراسة بعدد من الأشكال و الأمثلة و المخططات التي توضح محتوى الدراسة النظرية و ما قمنا بعمله في التطبيق ضمن بيئة الماتلاب .
Content Based Medical Image Retrieval (CBMIR) systems are a new technique which researchers aim to integrate with Computer Aided Diagnosis systems. These systems usually find and retrieve images from a large image-database which have a similar conten
t to a query image. Retrieval is done by extracting the visual features from the query image, formulating them in a features vector, comparing features vector components with those of the images in the database, and then, similarity measures are computed. Based on the similarity measures, images which have a similar content to the query image are retrieved. The introduced analysis study surveys and analyzes the current status of the CBMIR systems, evaluates our findings from this survey, and concludes some specific research directions in this field.
This research proposes a new way to improve the
search outcome of Arabic semantics by abstractly summarizing the
Arabic texts (Abstractive Summary) using natural language
processing algorithms(NLP),Word Sense Disambiguation (WSD)
and techniques o
f measuring Semantic Similarity in Arabic WordNet
Ontology.
معالجة اللغات الطبيعية
Semantic analysis
استرجاع المعلومات
التلخيص التجريدي
الأنتولوجيا العربية ووردنت
العلاقة الدلالية المفاهيمية
التشابهية الدلالية
التحليل الدلالي
حل غموض معاني الكلمات
(Natural Language Processing (NLP
(Information Retrieval (IR
Abstractive Summarization
(Arabic WordNet (AWN
Conceptual Semantic Relation
Semantic Similarity
(Word Sense Disambiguation (WSD
المزيد..
Term and glossary management are vital steps of preparation of every language specialist, and they play a very important role at the stage of education of translation professionals. The growing trend of efficient time management and constant time con
straints we may observe in every job sector increases the necessity of the automatic glossary compilation. Many well-performing bilingual AET systems are based on processing parallel data, however, such parallel corpora are not always available for a specific domain or a language pair. Domain-specific, bilingual access to information and its retrieval based on comparable corpora is a very promising area of research that requires a detailed analysis of both available data sources and the possible extraction techniques. This work focuses on domain-specific automatic terminology extraction from comparable corpora for the English -- Russian language pair by utilizing neural word embeddings.
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