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Jean-Christophe Burie and 9 others. 2015. ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc). 13th International Conference on Document Analysis and Recognition ICDAR2015.1161–1165.
Abstract: Smartphones are enabling new ways of capture,
hence arises the need for seamless and reliable acquisition and
digitization of documents, in order to convert them to editable,
searchable and a more human-readable format. Current stateof-the-art
works lack databases and baseline benchmarks for
digitizing mobile captured documents. We have organized a
competition for mobile document capture and OCR in order to
address this issue. The competition is structured into two independent
challenges: smartphone document capture, and smartphone
OCR. This report describes the datasets for both challenges
along with their ground truth, details the performance evaluation
protocols which we used, and presents the final results of the
participating methods. In total, we received 13 submissions: 8
for challenge-I, and 5 for challenge-2.
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Marçal Rusiñol, David Aldavert, Ricardo Toledo and Josep Llados. 2015. Towards Query-by-Speech Handwritten Keyword Spotting. 13th International Conference on Document Analysis and Recognition ICDAR2015.501–505.
Abstract: In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset.
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, Josep Llados, R.Jain and D.Doermann. 2015. Novel Line Verification for Multiple Instance Focused Retrieval in Document Collections. 13th International Conference on Document Analysis and Recognition ICDAR2015.481–485.
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Marçal Rusiñol, J. Chazalon, Jean-Marc Ogier and Josep Llados. 2015. A Comparative Study of Local Detectors and Descriptors for Mobile Document Classification. 13th International Conference on Document Analysis and Recognition ICDAR2015.596–600.
Abstract: In this paper we conduct a comparative study of local key-point detectors and local descriptors for the specific task of mobile document classification. A classification architecture based on direct matching of local descriptors is used as baseline for the comparative study. A set of four different key-point
detectors and four different local descriptors are tested in all the possible combinations. The experiments are conducted in a database consisting of 30 model documents acquired on 6 different backgrounds, totaling more than 36.000 test images.
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J. Chazalon, Marçal Rusiñol, Jean-Marc Ogier and Josep Llados. 2015. A Semi-Automatic Groundtruthing Tool for Mobile-Captured Document Segmentation. 13th International Conference on Document Analysis and Recognition ICDAR2015.621–625.
Abstract: This paper presents a novel way to generate groundtruth data for the evaluation of mobile document capture systems, focusing on the first stage of the image processing pipeline involved: document object detection and segmentation in lowquality preview frames. We introduce and describe a simple, robust and fast technique based on color markers which enables a semi-automated annotation of page corners. We also detail a technique for marker removal. Methods and tools presented in the paper were successfully used to annotate, in few hours, 24889
frames in 150 video files for the smartDOC competition at ICDAR 2015
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Dimosthenis Karatzas and 12 others. 2015. ICDAR 2015 Competition on Robust Reading. 13th International Conference on Document Analysis and Recognition ICDAR2015.1156–1160.
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Lluis Gomez and Dimosthenis Karatzas. 2015. Object Proposals for Text Extraction in the Wild. 13th International Conference on Document Analysis and Recognition ICDAR2015.206–210.
Abstract: Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available
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A.Nicolaou, Andrew Bagdanov, Marcus Liwicki and Dimosthenis Karatzas. 2015. Sparse Radial Sampling LBP for Writer Identification. 13th International Conference on Document Analysis and Recognition ICDAR2015.716–720.
Abstract: In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.
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Suman Ghosh and Ernest Valveny. 2015. Query by String word spotting based on character bi-gram indexing. 13th International Conference on Document Analysis and Recognition ICDAR2015.881–885.
Abstract: In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets
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R. Bertrand, Oriol Ramos Terrades, P. Gomez-Kramer, P. Franco and Jean-Marc Ogier. 2015. A Conditional Random Field model for font forgery detection. 13th International Conference on Document Analysis and Recognition ICDAR2015.576–580.
Abstract: Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters.
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