Browsing by Subject "Character recognition"
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Item Open Access COST292 experimental framework for TRECVID 2006(National Institute of Standards and Technology, 2006) Ćalić J.; Krämer P.; Naci, U.; Vrochidis, S.; Aksoy, S.; Zhangk Q.; Benois-Pineau J.; Saracoglu, A.; Doulaverakis, C.; Jarina, R.; Campbell, N.; Mezaris V.; Kompatsiaris I.; Spyrou, E.; Koumoulos G.; Avrithis, Y.; Dalkilic, A.; Alatan, A.; Hanjalic, A.; Izquierdo, E.In this paper we give an overview of the four TRECVID tasks submitted by COST292, European network of institutions in the area of semantic multimodal analysis and retrieval of digital video media. Initially, we present shot boundary evaluation method based on results merged using a confidence measure. The two SB detectors user here are presented, one of the Technical University of Delft and one of the LaBRI, University of Bordeaux 1, followed by the description of the merging algorithm. The high-level feature extraction task comprises three separate systems. The first system, developed by the National Technical University of Athens (NTUA) utilises a set of MPEG-7 low-level descriptors and Latent Semantic Analysis to detect the features. The second system, developed by Bilkent University, uses a Bayesian classifier trained with a "bag of subregions" for each keyframe. The third system by the Middle East Technical University (METU) exploits textual information in the video using character recognition methodology. The system submitted to the search task is an interactive retrieval application developed by Queen Mary, University of London, University of Zilina and ITI from Thessaloniki, combining basic retrieval functionalities in various modalities (i.e. visual, audio, textual) with a user interface supporting the submission of queries using any combination of the available retrieval tools and the accumulation of relevant retrieval results over all queries submitted by a single user during a specified time interval. Finally, the rushes task submission comprises a video summarisation and browsing system specifically designed to intuitively and efficiently presents rushes material in video production environment. This system is a result of joint work of University of Bristol, Technical University of Delft and LaBRI, University of Bordeaux 1.Item Open Access Handwritten mathematical formula recognition using a statistical approach(IEEE, 2011-04) Çelik, Mehmet; Yanıkoğlu, B.We present a probabilistic framework for a mathematical expression recognition system. The system is flexible in that its grammar can be extended easily, thanks to its graph grammar which eliminates the need for specifying rule precedence. It is also optimal in the sense that all possible interpretations of the expressions are expanded, without making early commitments or hard decisions. The current system is able to recognize shorter expressions well and in real time. In this paper, we give an overview of the whole system and describe in detail our context sensitive graph grammar and the parsing process.Item Open Access Hareket geçmişi görüntüsü yöntemi ile Türkçe işaret dilini tanima uygulaması(IEEE, 2016-05) Yalçınkaya, Özge; Atvar, A.; Duygulu, P.İşitme ve konuşma engelli bireylerin toplum içerisinde diger bireylerle sağlıklı şekilde iletişim kurabilmeleri açısından işaret dili çok önemli bir role sahiptir. Ne yazık ki işaret dilinin toplumda sadece duyarlı insanlar tarafından bilindiği ve bu sayının da azlıgı dikkat çekmektedir. Yaptığımız çalışma kapsamındaki amaç, geliştirdiğimiz sistem sayesinde işitme veya konuşma engeli mevcut olan bireylerin diğer bireylerle olan iletişiminde iyileşme sağlamaktır. Bu amaç doğrultusunda kameradan alınan işaret diline ait hareket bilgisi tanınabilmekte ve o hareketin ne anlama geldiği daha önceden eğitilen işaret diline ait hareket bilgileri ile karşılaştırılarak bulunabilmektedir. Hareket bilgilerinin kameradan alınan görüntülerden çıkarılması aşamasında "Hareket Geçmişi Görüntüsü" yöntemi kullanılmıştır. Bu bağlamdaki sınıflandırma işlemi için de "En Yakın Komşuluk" algoritması kullanılmıştır. Sonuç olarak geliştirilen sistem, eğitim kümesini kullanarak işaret dili hareketi için bir metin tahmin etmektedir. Toplamdaki sınıflandırma başarısı %95 olarak hesaplanmıştır.Item Open Access Multifont Ottoman character recognition(IEEE, 2000) Öztürk, Ali; Güneş, S.; Özbay, Y.Ottoman characters from three different fonts are used character recognition problem, broadly speaking, is transferring a page that contain symbols to the computer and matching these symbols with previously known or recognized symbols after extraction the features of these symbols via appropriate preprocessing methods. Because of silent features of the characters, implementing an Ottoman character recognition system is a difficult work. Different researchers have done lots of works for years to develop systems that would recognize Latin characters. Although almost one million people use Ottoman characters, great deal of whom has different native languages, the number of studies on this field is insufficient. In this study 28 different machine-printed to train the Artificial Neural Network and a %95 classification accuracy for the characters in these fonts and a %70 classification accuracy for a different font has been found.Item Open Access Osmanlıca kelimeleri eşleme(IEEE, 2007-06) Ataer, Esra; Duygulu, PınarOsmanlı arşivleri dünyanın pek çok yerinden araştırmacının ilgi alanına girmektedir. Fakat bu belgelerin elle çevirisi zor bir iş olduğu için, bu arşivler kullanılamaz durumdadır. Otomatik çeviri gerekmektedir, fakat Osmanlıca’nın yazma özelliklerinden dolayı karakter tabanlı tanıma sistemleri istenen başarıyı gösterememektedir. Ayrıca, belgeler minyatür ve tuğra gibi önemli kısımlar içerdiği için, imge formatında saklanmaları gerekmektedir. Bu nedenle, bu çalışmada Osmanlıca kelimeleri imge olarak görerek probleme imge erişim problemi olarak yaklaşıldı ve kelime eşleme tekniği üzerine bir çözüm önerisinde bulunuldu. Nesne tanımada başarılı olan görsel öğeler kümesi (bag-of-visterms) tekniği kelime eşleme işlemine uyarlandı ve böylece her kelime imgesi taç noktalarından çıkarılan SIFT özelliklerinin ¨ vektor¨ nicemlemesiyle sembolize edildi. Benzer kelimeler görsel ögelerin dağılımına göre eşlendi. Deneyler 10,000 kelimenin üzerindeki matbu ve elyazması belge üzerinde yapıldı. Sonuçlar sistemin benzer kelimeleri yüksek doğrulukla eşlediğini ve anlamsal benzerlikleri bulduğunu gösteriyor Large archives of Ottoman documents are challenging to many historians all over the world. However, these archives remain inaccessible since manual transcription of such a huge volume is difficult. Automatic transcription is required, but due to the characteristics of Ottoman documents, character recognition based systems may not yield satisfactory results. It is also desirable to store the documents in image form since the documents may contain important drawings, especially the signatures. Due to these reasons, in this study we treat the problem as an image retrieval problem with the view that Ottoman words are images, and we propose a solution based on image matching techniques. The bag-of-visterms approach, which is shown to be successful to classify objects and scenes, is adapted for matching word images. Each word image is represented by a set of visual terms which are obtained by vector quantization of SIFT descriptors extracted from salient points. Similar words are then matched based on the similarity of the distributions of the visual terms. The experiments are carried out on printed and handwritten documents which included over 10,000 words. The results show that, the proposed system is able to retrieve words with high accuracies, and capture the semantic similarities between words.Item Open Access OTAP Ottoman archives internet interface(IEEE, 2012) Şahin, Emre; Adıgüzel, Hande; Duygulu, Pınar; Kalpaklı, MehmetWithin Ottoman Text Archive Project a web interface to aid in uploading, binarization, line and word segmentation, labeling, recognition and testing of the Ottoman Turkish texts has been developed. It became possible to retrieve expert knowledge of scholars working with Ottoman archives through this interface, and apply this knowledge in developing further technologies in transliteration of historical manuscripts. © 2012 IEEE.Item Open Access Redif extraction in handwritten Ottoman literary texts(IEEE, 2010) Can, Ethem F.; Duygulu, Pınar; Can, Fazlı; Kalpaklı, MehmetRepeated patterns, rhymes and redifs, are among the fundamental building blocks of Ottoman Divan poetry. They provide integrity of a poem by connecting its parts and bring a melody to its voice. In Ottoman literature, poets wrote their works by making use of the rhymes and redifs of previous poems according to the nazire (creative imitation) tradition either to prove their expertise or to show respect towards old masters. Automatic recognition of redifs would provide important data mining opportunities in literary analyses of Ottoman poetry where the majority of it is in handwritten form. In this study, we propose a matching criterion and method, Redif Extraction using Contour Segments (RECS) using the proposed matching criterion, that detects redifs in handwritten Ottoman literary texts using only visual analysis. Our method provides a success rate of 0.682 in a test collection of 100 poems. © 2010 IEEE.Item Open Access Vision-based single-stroke character recognition for wearable computing(IEEE, 2001) Özer, Ö. F.; Özün, O.; Tüzel, C. Ö.; Atalay, V.; Çetin, A. EnisParticularly when compared to traditional tools such as a keyboard or mouse, wearable computing data entry tools offer increased mobility and flexibility. Such tools include touch screens, hand gesture and facial expression recognition, speech recognition, and key systems. We describe a new approach for recognizing characters drawn by hand gestures or by a pointer on a user's forearm captured by a digital camera. We draw each character as a single, isolated stroke using a Graffiti-like alphabet. Our algorithm enables effective and quick character recognition. The resulting character recognition system has potential for application in mobile communication and computing devices such as phones, laptop computers, handheld computers and personal data assistants.