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  • ICDAR2015 TC10-11 Joint Meeting - TC11
    Report 13 15 13 20 ICDAR2017 Report 13 20 13 25 TC11 report 13 25 13 30 TC10 report 13 30 13 45 Discussion 13 45 13 50 Closing Retrieved from http www iapr tc11 org mediawiki index php title ICDAR2015 TC10 11 Joint Meeting oldid 2218 Personal tools 91 105 69 17 Talk for this IP address Log in create account Namespaces Page Discussion Variants Views Read View source

    Original URL path: http://www.iapr-tc11.org/mediawiki/index.php/ICDAR2015_TC10-11_Joint_Meeting (2016-02-15)
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  • Call for proposal to host ICDAR2019 - TC11
    of Document Analysis and Recognition Any consortium interested in making a proposal to host an ICDAR should first familiarise themselves with the Guidelines for Organizing and Bidding to Host ICDAR document which is available on the TC10 and TC11 websites www iapr tc10 org and www iapr tc11 org respectively A link to the most current version of the guidelines appears below http www iapr tc11 org mediawiki images ICDAR Guidelines 2011 04 04 pdf The submission of a bid implies full agreement with the rules and procedures outlined in that document The submitted proposal must define clearly the items specified in the guidelines Section 5 2 It has been the tradition that the location of ICDAR conferences follows a rotating schedule among different continents Hence proposals from America are encouraged However high quality bids from other locations for example from countries where we have had no ICDAR before will also be considered Proposals will be examined by the ICDAR Advisory Board Proposals should be emailed to Prof Koichi Kise at kise cs osakafu u ac jp by June 15 2015 ICDAR Advisory Board Prof Koichi Kise Chair TC11 Prof Rafael Dueire Lins Chair TC10 Prof Dan Lopresti Chair C

    Original URL path: http://www.iapr-tc11.org/mediawiki/index.php/Call_for_proposal_to_host_ICDAR2019 (2016-02-15)
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  • years two awards categories are presented Namely the IAPR ICDAR Young Investigator Award less than 40 years old at the time the award is made and the IAPR ICDAR Outstanding Achievements Award Each award will consist of a token gift and a suitably inscribed certificate The recipient of the Outstanding Achievements award will be invited to give the opening key note speech at the ICDAR 2015 conference introduced by the recipient from the previous conference Nominations are invited for the ICDAR 2015 Awards in both categories The nomination packet should include the following 1 A nominating letter 1 page including a brief citation to be included in the certificate 2 A brief vitae 2 pages of the nominee highlighting the accomplishments being recognized 3 Supporting letters 1 page each from 3 active researchers from at least 3 different countries A nomination is usually put forward by a researcher preferably from a different Institution than the nominee who is knowledgeable of the scientific achievements of the nominee and who organizes letters of support Submission procedure is strictly confidential and self nominations are not allowed Please send nominations packets electronically to the Awards Committee Co Chairs Koichi Kise and Rafael Lins The

    Original URL path: http://www.iapr-tc11.org/mediawiki/images/ICDAR2015_call_for_award_nominations.txt (2016-02-15)
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  • ICDAR2011 Doctoral Consortium - TC11
    1 40 pm 2 10 pm Welcome and short talk Advice for a Successful Ph D Experience 2 10 pm 3 10 pm Brief oral introductions to student research plans 3 10 pm 3 25 pm Coffee break 3 25 pm 5 25 pm Student poster session with discussion and feedback 5 25 pm 5 40 pm Concluding remarks and Best Poster Presentation Award presentation A Doctoral Consortium overview booklet was made available at the event containing a summary of each student s research activities along with his her resume Best Poster Presentation Award A Best Poster Presentation Award was be given at the end of the day the winners of this award were Nibal Nayef Technical University of Kaiserslautern Geometric based Symbol Spotting with Application to Symbol Retrieval in Document Image Databases Weihan Sun Osaka Prefecture University Copyright Protection of Manga Using Content based Image Retrieval Methods Participants Participating Ph D Students The list of participating Ph D students is as follows Name Research Topic University Country Poster Olivier Augereau Document Image Classification Université Bordeaux France Su Bolan Document Image Enhancement National University of Singapore Singapore Klaus Broelemann Automatic Understanding of Sketch Maps University of Muenster Germany Syed Saqib Bukhari Generic Layout Analysis of Diverse Collection of Documents Technical University of Kaiserslautern Germany Bin Chen Effects of Artificial Sample Generation Models for On line Handwritten Japanese Character Recognition Tokyo University of Agriculture and Technology Japan Jin Chen Exploiting Metadata in Off line Handwritten Documents Modeling and Applications Lehigh University USA Lluís Pere de las Heras Syntactic Model for Semantic Document Analysis Universitat Autònoma de Barcelona Spain Jing Fang Table Recognition and Evaluation in PDF Documents Peking University China David Hebert Investigations on the Use of Linear Chain CRF Based Method to Segment Old Newspapers Universite de Rouen France Lei Hu Recognition and Retrieval of Handwritten Mathematical Expressions Rochester Institute of Technology USA Le Kang Touching Text Segmentation and Shape Analysis University of Maryland College Park USA Muna Khayyat Learning Based Word Spotting for Arabic Handwritten Documents Using Hierarchical Classifier Concordia University Canada Iuliu Konya Adaptive Methods for Robust Document Image Understanding Fraunhofer IAIS University of Bonn Germany Jayant Kumar Segmentation and Labeling of Mixed type Noisy Handwritten Documents University of Maryland College Park USA Xiaoyan Lin Mathematical Formula Recognition and Retrieval in PDF Documents Peking University China Muhammad Muzzamil Luqman Efficient Indexing and Retrieval of Graphs Using Techniques for Embedding Graphs in Real Valued Feature Spaces Université François Rabelais de Tours France Nibal Nayef Geometric based Symbol Spotting with Application to Symbol Retrieval in Document Image Databases T U Kaiserslautern Germany Weihan Sun Copyright Protection of Manga Using Content based Image Retrieval Methods Osaka Prefecture University Japan Rabeux Vincent Document Image Quality Evaluation Université Bordeaux France Song Wang Part Based Method of Character Recognition Kyushu University Japan Liang Xu Segmentation and Recognition of Touching Characters in Offline Unconstrained Chinese Handwriting Institute of Automation Chinese Academy of Sciences China Mentors In addition we wish to thank the mentors who have

    Original URL path: http://www.iapr-tc11.org/mediawiki/index.php/ICDAR2011_Doctoral_Consortium (2016-02-15)
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  • Bids to Host ICDAR2015 - TC11
    in Tunisia Adel Alimi et al The ICDAR Advisory Board has contacted all of the main organizers named in the above bid regarding their service in the indicated roles if the bid is awarded with the exception of those marked TBC to be confirmed Retrieved from http www iapr tc11 org mediawiki index php title Bids to Host ICDAR2015 oldid 1487 Personal tools 91 105 69 17 Talk for this

    Original URL path: http://www.iapr-tc11.org/mediawiki/index.php/Bids_to_Host_ICDAR2015 (2016-02-15)
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  • KAIST Scene Text Database - TC11
    mobile phone camera All images have been resized to 640x480 The KAIST scene text database is categorized according to the language of the scene text captured Korean English Number and Mixed Korean English Number The scene text in the images is representative of common text in Korean streets or shops Related Ground Truth Data KAIST Scene Text Ground Truth text location segmantation and recognition Related Tasks Scene Text Localisation in the KAIST Dataset Scene Text Segmentation in the KAIST Dataset References Jehyun Jung SeongHun Lee Min Su Cho and Jin Hyung Kim Touch TT Scene Text Extractor Using Touch Screen Interface ETRI Journal 2011 SeongHun Lee Min Su Cho Kyomin Jung and Jin Hyung Kim Scene Text Extraction with Edge Constraint and Text Collinearity Link 20th International Conference on Pattern Recognition ICPR August 2010 Istanbul Turkey Submitted Files Version 1 0 Complete Download the directory structure of the zip file reflects the structure below 364 MB Korean Language Digital Camera Signboard Shadow 6 87 MB Light 1 14 MB Outdoor 1 21 51 MB Outdoor 2 19 79 MB Outdoor 3 13 13 MB Outdoor 4 13 00 MB Outdoor 5 12 86 MB Outdoor 6 12 84 MB Outdoor 7 13 71 MB Outdoor 8 4 52 MB Indoor 1 14 14 MB Indoor 2 9 52 MB Night 6 70 MB Book Cover 6 18 MB Others 7 93 MB Mobile Phone Signboard Shadow 645 54 KB Light 336 4 KB Outdoor 2 43 MB Indoor 1 40 MB Book Cover 18 63 MB Others 430 55 KB English Language Digital Camera Signboard Shadow 2 16 MB Light 2 33 MB Outdoor 1 8 44 MB Outdoor 2 10 53 MB Outdoor 3 2 46 MB Indoor 8 44 MB Night 2 32 MB Others 6 17 MB

    Original URL path: http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database (2016-02-15)
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  • IBN SINA: A database for research on processing and understanding of Arabic manuscripts images - TC11
    can be found in reference 3 For the skeletonization a thinning process has been used followed by a correction step to discover missed branch points Version 1 0 The first version of the dataset comprises the feature vectors of the 20722 shapes sub words extracted as connected components The feature vector consists of 92 features The features can be divided in two parts 1 8 global features and 2 84 skeleton based features The second part also can be divided in two sub parts a topological features based on the relation to the branch end singular points on the skeleton and b geometrical features related to the orientation and position of sub strokes that comprise the connected component or shape under study The feature vector is regularized in terms of its length The details can be found in section 4 of the paper 1 All non normalised feature vectors are provided in a single space delimited text file where each row corresponds to the feature vector of a single connected component Version 2 0 The extended version of the dataset includes the corresponding image data of the shapes The dataset comprises a series of MatLAB files one for each folio of the manuscript containing a structure with all the available information icluding the images and features of each sub word The metadata provided include the feature vector calculated from the skeleton of shapes 1 Detailed information about the structure of the files is provided in the guide Two editions of the dataset v2 0 are provided for greater flexibility one containing just the binarized images of the shapes small file and another containing both the original color images and the binarized ones large file The rest of the information in the both files is identical Related Ground Truth Data Version 1 0 For each shape in the dataset 15 binary labels that correspond to 15 problems are provided For each problem the label specifies whether the string of that shape contains the associate Arabic letter of that problem or not For example in Latin language for the sake of font simplicity if the string is ab for Problem of letter a the label will be 1 and for problem of letter c the label will be 1 Only 15 letters are considered here that have at least 1000 positive samples each in the dataset The 15 letters are Ein abbreviated as EU Aleph abbreviated as aL Be Abbreviated as bL Dal Abbreviated as dL Fa Abbreviated as fL Ha Abbreviated as hL Kaf Abbreviated as kL Lam Abbreviated as lL Mim Abbreviated as mL Nun Abbreviated as nL Ghain Abbreviated as qL Ra Abbreviated as rL Ta Abbreviated as tL Waw Abbreviated as vL Ya Abbreviated as yL For abbreviation of the problem names Fingilish encoding is used which corresponds each Arabic letter to an ASCII character Please see the report for the details Ground Truth Specification The ground truth data are specified as a single 20722 X 15 matrix

    Original URL path: http://www.iapr-tc11.org/mediawiki/index.php/IBN_SINA:_A_database_for_research_on_processing_and_understanding_of_Arabic_manuscripts_images (2016-02-15)
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  • Harbin Institute of Technology Opening Recognition Corpus for Chinese Characters (HIT-OR3C) - TC11
    handwriting pad and are recorded and labelled automatically via the handwriting document collection software OR3C Toolkit The software used to collect the characters is also made available supplied version is in Chinese The dataset is organised in 5 subsets 4 subsets of characters Digit 1 10 Letter 11 62 GB1 63 3817 GB2 3818 6825 and 1 subset of documents The 4 subsets of characters contain 6 825 classes produced by 122 subjects and 832 650 samples in total A single file per subject is provided for online data and a single file per subject for offline data see below for the file format used The different subsets are defined as index ranges within these files The document corpus corresponds to 10 news articles that contain in total 77 168 samples drawn from 2 442 classes and produced by 20 subjects The document captured data have been post processed and split into individual characters the characters resized to 128 x 128 pixels and stored sequentially in a single image and a single vector file similarly to the first four subsets The dataset contains 909 818 images The total size of the dataset is 15 5 GB 1125 Mb compressed There are three file formats defined by ourselves and introduced in the related documents The individual character images are 128 x 128 greyscale Metadata For each image a label is provided The labels of digits and letters are encoded in ASCII the labels of Chinese characters are encoded in GB2312 80 The label file is in every folder and named labels txt Related Ground Truth Data N A Related Tasks Handwriting recognition for Chinese characters References S Zhou Q Chen X Wang HIT OR3C An Opening Recognition Corpus for Chinese Characters DAS 2010 to appear Version Correspondence Dataset Task V1 0

    Original URL path: http://www.iapr-tc11.org/mediawiki/index.php/Harbin_Institute_of_Technology_Opening_Recognition_Corpus_for_Chinese_Characters_%28HIT-OR3C%29 (2016-02-15)
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