96 research outputs found
Fuzzy Multilevel Graph Embedding for Recognition, Indexing and Retrieval of Graphic Document Images
No full textAdvisors: Jean-Yves Ramel, Josep Lladós and Thierry Brouard Date and location of PhD thesis defense: 2nd of March 2012 at University of Tours in France.This thesis addresses the problem of lack of efficient computational tools for graph based structural pattern recognition approaches and proposes to exploit computational strength of statistical pattern recognition. It has two fold contributions. The first contribution is a new method of explicit graph embedding. The proposed graph embedding method exploits multilevel analysis of graph for extracting graph level information, structural level information and elementary level information from graphs. It embeds this information into a numeric feature vector. The method employs fuzzy overlapping trapezoidal intervals for addressing the noise sensitivity of graph representations and for minimizing the information loss while mapping from continuous graph space to discrete vector space. The method has unsupervised learning abilities and is capable of automatically adapting its parameters to underlying graph dataset. The second contribution is a framework for automatic indexing of graph repositories for graph retrieval and subgraph spotting. This framework exploits explicit graph embedding for representing the cliques of order 2 by numeric feature vectors, together with classification and clustering tools for automatically indexing a graph repository. It does not require a labeled learning set and can be easily deployed to a range of application domains, offering ease of query by example (QBE) and granularity of focused retrieval
ICDAR2015 competition on smartphone document capture and OCR (SmartDoc) - Challenge 2
No full textICDAR2015 competition on smartphone document capture and OCR (SmartDoc)
Challenge 2: MOBILE OCR COMPETITION
The goal of the competition is to extract the textual content from document images which are captured by mobile phones. The images are taken under varying conditions to provide a challenging input. The dataset was prepared for ICDAR2015-SmartDoc competition. For more details about the dataset please visit the competition's website:
https://sites.google.com/site/icdar15smartdoc/home
http://smartdoc.univ-lr.fr
You may also refer to the following paper for more details on the ICDAR2015-SmartDoc competition:
Jean-Christophe Burie, Joseph Chazalon, Mickaël Coustaty, Sébastien Eskenazi, Muhammad Muzzamil Luqman, Maroua Mehri, Nibal Nayef, Jean-Marc OGIER, Sophea Prum and Marçal Rusinol: “ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc)”, In 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.
If you use this dataset, please send us a short email at to tell us why it was useful to you, and whether you have results or publications we can reference on our website. Thank you!</p
Kajian Balaghah Dalam Al-Qur’an Surat Luqman
No full textThe science of Balaghah is in fact not only examined by text. Long before that, it turns out that in the Qur'an there are the beauty of languages that the Arab poet could not have matched though. More interesting, the beauty and style of language in the surah Luqman when studied with the science of Balaghah contains three important elements in human life. Namely, the education of Aqidah, sharia and morality. In this study Balaghah science is examined specifically and in depth by the author with ilmu Ma'ani. In the science of Ma'ani, the authors deepen the study of Insha Kalam discussing Amr Balaghi. And at the end of this study the author found 2 out of 9 kinds of Balaghi Amr, both of which have the meaning of al-Irsyad and at-Tahdid. Amr has the meaning of al-Irsyad found in verses 12, 14, 15, 17, 19, 21. While Amr has the meaning of at-Tahdid in verses 7 and 33. And all the meanings of al-Irsyad and at-Tahdid this can assert the meaning of the content of the surah Luqman itself. So in the end, it can help in human life. At the same time, it confirms that the Qur'an is a miracle for human understand
SmartDoc-QA: A dataset for quality assessment of smartphone captured document images - single and multiple distortions
No full textModern smartphones have a revolutionary impact on the way people digitize the paper documents. The wide ownership of smartphones and their ease of use for digitizing paper documents has resulted into massive amount of imagery data of digitized paper documents. The goal of digitizing the paper documents is not only to archive them for sharing but also, most of the times, to process them by automated document image processing systems. The latter extracts the content of the document images for recognizing it, indexing it, verifying it, comparing it with a database etc. However, it is a known fact that the cameras of the smartphones are optimized for capturing natural scene images. Taking a simple photo of a paper document does not ensure that its content would be exploitable by automated document image processing systems. This could happen because of the light conditions, the resolution of the image, the camera noise, the perspective distortion, the physical distortions (folds etc.) of the paper, the out-of-focus blur and/or the motion blur during capture. To ensure that the content of a captured document image is exploitable by automated systems, it is important to automatically assess the quality of a captured document image in real-time. Otherwise most of the times it is not possible to re-capture the document image later on, because the original document is not available anymore. Assessing the quality of a captured document image is also required in situations where the captured document images are to-be transmitted for further processing.
The quality assessment step is an important part of both the acquisition and the digitization processes. Assessing document quality could aid users during the capture process or help improve image enhancement methods after a document has been captured. Current state-of-the-art works lack databases in the field of document image quality assessment.
In order to provide a baseline benchmark for quality assessment methods for mobile captured documents, we present a database for quality assessment that contains both single- and multiply-distorted document images.
The proposed dataset could be used for benchmarking quality assessment methods by the objective measure of OCR accuracy, and could be also used to benchmark quality enhancement methods. There are three types of documents in the dataset: modern documents, old administrative letters and receipts.
The document images of the dataset are captured under varying capture conditions (light, different types of blur and perspective angles). This causes geometric and photometric distortions that hinder the OCR process.
The ground truth of the dataset set images consists of the text transcriptions of the documents, the OCR results of the captured documents and the values of the different capture parameters used for each image.
Any use of this dataset is required to cite the following reference:
Nibal Nayef, Muhammad Muzzamil Luqman, Sophea Prum, Sebastien Eskenazi, Joseph Chazalon, Jean-Marc Ogier: “SmartDoc-QA: A Dataset for Quality Assessment of Smartphone Captured Document Images - Single and Multiple Distortions”, Proceedings of the sixth international workshop on Camera Based Document Analysis and Recognition (CBDAR), 2015
Fuzzy Multilevel Graph Embedding for Recognition, Indexing and Retrieval of Graphic Document Images
No full textAdvisors: Jean-Yves Ramel, Josep Lladós and Thierry Brouard Date and location of PhD thesis defense: 2nd of March 2012 at University of Tours in France.This thesis addresses the problem of lack of efficient computational tools for graph based structural pattern recognition approaches and proposes to exploit computational strength of statistical pattern recognition. It has two fold contributions. The first contribution is a new method of explicit graph embedding. The proposed graph embedding method exploits multilevel analysis of graph for extracting graph level information, structural level information and elementary level information from graphs. It embeds this information into a numeric feature vector. The method employs fuzzy overlapping trapezoidal intervals for addressing the noise sensitivity of graph representations and for minimizing the information loss while mapping from continuous graph space to discrete vector space. The method has unsupervised learning abilities and is capable of automatically adapting its parameters to underlying graph dataset. The second contribution is a framework for automatic indexing of graph repositories for graph retrieval and subgraph spotting. This framework exploits explicit graph embedding for representing the cliques of order 2 by numeric feature vectors, together with classification and clustering tools for automatically indexing a graph repository. It does not require a labeled learning set and can be easily deployed to a range of application domains, offering ease of query by example (QBE) and granularity of focused retrieval
Apport des modèles graphiques à l'analyse et à l'indexation d'images de documents
No full textCette thèse aborde le problème du manque de performance des outils exploitant des représentationsà base de graphes en reconnaissance des formes. Nous proposons de contribuer aux nouvellesméthodes proposant de tirer partie, à la fois, de la richesse des méthodes structurelles et de la rapidité des méthodes de reconnaissance de formes statistiques. Deux principales contributions sontprésentées dans ce manuscrit. La première correspond à la proposition d'une nouvelle méthode deprojection explicite de graphes procédant par analyse multi-facettes des graphes. Cette méthodeeffectue une caractérisation des graphes suivant différents niveaux qui correspondent, selon nous,aux point-clés des représentations à base de graphes. Il s'agit de capturer l'information portéepar un graphe au niveau global, au niveau structure et au niveau local ou élémentaire. Ces informationscapturées sont encapsulés dans un vecteur de caractéristiques numériques employantdes histogrammes flous. La méthode proposée utilise, de plus, un mécanisme d'apprentissage nonsupervisée pour adapter automatiquement ses paramètres en fonction de la base de graphes àtraiter sans nécessité de phase d'apprentissage préalable. La deuxième contribution correspondà la mise en place d'une architecture pour l'indexation de masses de graphes afin de permettre,par la suite, la recherche de sous-graphes présents dans cette base. Cette architecture utilise laméthode précédente de projection explicite de graphes appliquée sur toutes les cliques d'ordre 2pouvant être extraites des graphes présents dans la base à indexer afin de pouvoir les classifier.Cette classification permet de constituer l'index qui sert de base à la description des graphes etdonc à leur indexation en ne nécessitant aucune base d'apprentissage pré-étiquetées. La méthodeproposée est applicable à de nombreux domaines, apportant la souplesse d'un système de requêtepar l'exemple et la granularité des techniques d'extraction ciblée (focused retrieval).This thesis addresses the problem of lack of efficient computational tools for graph based structural pattern recognition approaches and proposes to exploit computational strength of statistical pattern recognition. It has two fold contributions. The first contribution is a new method of explicit graph embedding. The proposed graph embedding method exploits multilevel analysis of graph for extracting graph level information, structural level information and elementary level information from graphs. It embeds this information into a numeric feature vector. The method employs fuzzy overlapping trapezoidal intervals for addressing the noise sensitivity of graph representations and for minimizing the information loss while mapping from continuous graph space to discrete vector space. The method has unsupervised learning abilities and is capable of automatically adapting its parameters to underlying graph dataset. The second contribution is a framework for automatic indexing of graph repositories for graph retrieval and subgraph spotting. This framework exploits explicit graph embedding for representing the cliques of order 2 by numeric feature vectors, together with classification and clustering tools for automatically indexing a graph repository. It does not require a labeled learning set and can be easily deployed to a range of application domains, offering ease of query by example (QBE) and granularity of focused retrieval
ANALISIS METODE DAN NILAI PENDIDIKAN ISLAM DALAM SURAT LUQMAN AYAT 12-19
No full textABSTRACT
Al-Qur'an is a guide that comes from Allah SWT that must be
understood, lived and practiced by humans who believe in Allah SWT. In the
Qur'an there are various methods and educational values that can be taken as a
lesson reference for educators. With the methods and educational values in the
Qur'an, the Qur'an is the main source used in the world of Islamic education as a
means of instilling the values contained in the Qur'an to students. Among the
various verses in the Qur'an that contain educational methods and values, one of
them is Surah Luqman verses 12-19. In conducting research, the author will
examine the methods and content of educational values contained in the letter
Luqman verses 12-19. In addition, this study can provide a description that in the
Qur'an there are various methods used as well as educational values, so that they
can be an inspiration for educators in an effort to develop the world of education
by referring to the Qur'an as the main source in teaching and learning. Islam.
This research is included in the type of library research (Library
research). In obtaining the data, the writer collected data from primary sources,
namely the Qur'an and its translation, as well as Tafsir Ibn Kathir Volume 7 by
Shafiyyurrahman al-Mubarakfuri, Tafsir Al-Azhar volume 7 by Hamka, Tafsir
Al-Munir volume 11 by Wahbah Az. -Zuhaili, Tafsir Al-Quranul Majid An-Nur
Volume 3 by Teungku Muhammad Hasbi ash-Shiddieqy, and Tafsir Tarbawi by
Kadar M. Yusuf in Luqman verse 12-19 as well as various secondary sources that
support data analysis. In analyzing the data, the author uses the method of
content analysis (Content analysis). This content analysis is to find out the verbal
meaning used to obtain information from the content conveyed.
The results of this study indicate that in the letter Luqman verses 12-19
there are several methods used in instilling the values contained in verses 12-19,
namely: 1) the role model method, 2) the mau'idhah method. In addition, other
results from this study are the existence of various educational values, namely: 1)
the value of gratitude, 2) the value of prohibiting shirk, 3) the value of filial piety
to both parents, 4) every act will be rewarded, 5) establish prayer, amar ma'ruf
nahi munkar, 6) prohibition of being arrogant, 7) manners of walking and talking.
Keywords: Methods, Educational Values, and Surah Luqman verses 12-1
Dynamic Deep Multi-task Learning for Caricature-Visual Face Recognition
No full textInternational audienc
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
No full text5 pages, Tenth International Conference on Document Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10, 1325-1329International audienceWe present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates
NILAI-NILAI SUFISTIK DALAM SURAT LUQMAN AYAT 12-19 (KAJIAN TAFSIR AL-JAILANI)
No full textThe development of Qur’an interpretation started from the time of the Prophet, his companions, tabi'in, and tabial-tabi'in, The dominant interpretation of the Qur'an were the tafsir of bi al-ma'tsur, and tafsir bial-ra'yi,. However, according to some scholars of the Qur'an, the interpretation of Al-Qur’an is mapped into three, namely the interpretation that refers to the history (bi al-ma'sur), based on the reasoning (bi al-ra'yi), and using an intuition (bi al-isyari). Furthermore, among the three sources of interpretation above, the interpretation of intuition (bi al-isyari) is called Sufi interpretation. Tafsir Al-Jailani is one of  Sufi interpretation. This paper aims to study the Sufi values in surah of Luqman. In this research, the author uses the method of literature study with a philosophical approach. The source of data in this study comes from tafsir with a Sufi pattern. The conclusion can be drawn is that in surah of Luqman 12-19 there are sufistic values namely: 1) The Meaning of Wisdom, 2) The Meaning of Gratitude, 3) The Meaning of Shirk, 4) Gratitude to Parents, 5) Following the Path of the Repentant, 6) Amar Ma'ruf Nahyi Munkar, 7) The Meaning of Patience, 8) Arrogant Deeds, and 9) The Meaning of Lowering  Voice.Keywords: Sufism, Tafsir Al-Jailani, Surah Luqma
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