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Andreas Fischer, Ching Y. Suen, Volkmar Frinken, Kaspar Riesen, & Horst Bunke. (2013). A Fast Matching Algorithm for Graph-Based Handwriting Recognition. In 9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition (Vol. 7877, pp. 194–203). LNCS. Springer Berlin Heidelberg.
Abstract: The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.
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Andreas Fischer, Volkmar Frinken, Horst Bunke, & Ching Y. Suen. (2013). Improving HMM-Based Keyword Spotting with Character Language Models. In 12th International Conference on Document Analysis and Recognition (pp. 506–510).
Abstract: Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance.
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Albert Gordo, Florent Perronnin, Yunchao Gong, & Svetlana Lazebnik. (2014). Asymmetric Distances for Binary Embeddings. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 33–47.
Abstract: In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.
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Dimosthenis Karatzas, Faisal Shafait, Seiichi Uchida, Masakazu Iwamura, Lluis Gomez, Sergi Robles, et al. (2013). ICDAR 2013 Robust Reading Competition. In 12th International Conference on Document Analysis and Recognition (pp. 1484–1493).
Abstract: This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.
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Christophe Rigaud, Dimosthenis Karatzas, Jean-Christophe Burie, & Jean-Marc Ogier. (2013). Speech balloon contour classification in comics. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: Comic books digitization combined with subsequent comic book understanding create a variety of new applications, including mobile reading and data mining. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. In this work we detail a novel approach for classifying speech balloon in scanned comics book pages based on their contour time series.
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, Josep Llados, Tomokazu Sato, Masakazu Iwamura, et al. (2013). Key-region detection for document images -applications to administrative document retrieval. In 12th International Conference on Document Analysis and Recognition (pp. 230–234).
Abstract: In this paper we argue that a key-region detector designed to take into account the special characteristics of document images can result in the detection of less and more meaningful key-regions. We propose a fast key-region detector able to capture aspects of the structural information of the document, and demonstrate its efficiency by comparing against standard detectors in an administrative document retrieval scenario. We show that using the proposed detector results to a smaller number of detected key-regions and higher performance without any drop in speed compared to standard state of the art detectors.
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David Fernandez, Simone Marinai, Josep Llados, & Alicia Fornes. (2013). Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks. In 2nd International Workshop on Historical Document Imaging and Processing (pp. 36–43).
Abstract: Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information.
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Pau Riba, Jon Almazan, Alicia Fornes, David Fernandez, Ernest Valveny, & Josep Llados. (2014). e-Crowds: a mobile platform for browsing and searching in historical demographyrelated manuscripts. In 14th International Conference on Frontiers in Handwriting Recognition (pp. 228–233).
Abstract: This paper presents a prototype system running on portable devices for browsing and word searching through historical handwritten document collections. The platform adapts the paradigm of eBook reading, where the narrative is not necessarily sequential, but centered on the user actions. The novelty is to replace digitally born books by digitized historical manuscripts of marriage licenses, so document analysis tasks are required in the browser. With an active reading paradigm, the user can cast queries of people names, so he/she can implicitly follow genealogical links. In addition, the system allows combined searches: the user can refine a search by adding more words to search. As a second contribution, the retrieval functionality involves as a core technology a word spotting module with an unified approach, which allows combined query searches, and also two input modalities: query-by-example, and query-by-string.
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Jon Almazan, Albert Gordo, Alicia Fornes, & Ernest Valveny. (2014). Word Spotting and Recognition with Embedded Attributes. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(12), 2552–2566.
Abstract: This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
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Volkmar Frinken, Andreas Fischer, & Carlos David Martinez Hinarejos. (2013). Handwriting Recognition in Historical Documents using Very Large Vocabularies. In 2nd International Workshop on Historical Document Imaging and Processing (pp. 67–72).
Abstract: Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words.
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Marçal Rusiñol, V. Poulain d'Andecy, Dimosthenis Karatzas, & Josep Llados. (2013). Classification of Administrative Document Images by Logo Identification. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier's graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents.
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, Apostolos Antonacopoulos, & Josep Llados. (2013). An interactive appearance-based document retrieval system for historical newspapers. In Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 84–87).
Abstract: In this paper we present a retrieval-based application aimed at assisting a user to semi-automatically segment an incoming flow of historical newspaper images by automatically detecting a particular type of pages based on their appearance. A visual descriptor is used to assess page similarity while a relevance feedback process allow refining the results iteratively. The application is tested on a large dataset of digitised historic newspapers.
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Marçal Rusiñol, V. Poulain d'Andecy, Dimosthenis Karatzas, & Josep Llados. (2014). Classification of Administrative Document Images by Logo Identification. In Bart Lamiroy, & Jean-Marc Ogier (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 49–58). Springer Berlin Heidelberg.
Abstract: This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier’s graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents.
Keywords: Administrative Document Classification; Logo Recognition; Logo Spotting
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Antonio Clavelli, Dimosthenis Karatzas, Josep Llados, Mario Ferraro, & Giuseppe Boccignone. (2014). Modelling task-dependent eye guidance to objects in pictures. CoCom - Cognitive Computation, 6(3), 558–584.
Abstract: 5Y Impact Factor: 1.14 / 3rd (Computer Science, Artificial Intelligence)
We introduce a model of attentional eye guidance based on the rationale that the deployment of gaze is to be considered in the context of a general action-perception loop relying on two strictly intertwined processes: sensory processing, depending on current gaze position, identifies sources of information that are most valuable under the given task; motor processing links such information with the oculomotor act by sampling the next gaze position and thus performing the gaze shift. In such a framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the payoff of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. The different levels of the action-perception loop are represented in probabilistic form and eventually give rise to a stochastic process that generates the gaze sequence. This way the model also accounts for statistical properties of gaze shifts such as individual scan path variability. Results of the simulations are compared either with experimental data derived from publicly available datasets and from our own experiments.
Keywords: Visual attention; Gaze guidance; Value; Payoff; Stochastic fixation prediction
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, & Josep Llados. (2014). Fast Structural Matching for Document Image Retrieval through Spatial Databases. In Document Recognition and Retrieval XXI (Vol. 9021).
Abstract: The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors.
Keywords: Document image retrieval; distance transform; MSER; spatial database
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