Querying out-of-vocabulary words in lexicon-based keyword spotting

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2197-8[EN] Lexicon-based handwritten text keyword spotting (KWS) has proven to be a faster and more accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS relies on a predefined vocabulary, fixed in the training phase, it does not support queries involving out-of-vocabulary (OOV) keywords. In this paper, we outline previous work aimed at solving this problem and present a new approach based on smoothing the (null) scores of OOV keywords by means of the information provided by ``similar'' in-vocabulary words. Good results achieved using this approach are compared with previously published alternatives on different data sets.This work was partially supported by the Spanish MEC under FPU Grant FPU13/06281, by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMA-MATER, and through the EU Projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).Puigcerver, J.; Toselli, AH.; Vidal, E. (2016). Querying out-of-vocabulary words in lexicon-based keyword spotting. Neural Computing and Applications. 1-10. https://doi.org/10.1007/s00521-016-2197-8S110Almazan J, Gordo A, Fornes A, Valveny E (2013) Handwritten word spotting with corrected attributes. In: 2013 IEEE international conference on computer vision (ICCV), pp 1017–1024. doi: 10.1109/ICCV.2013.130Amengual JC, Vidal E (2000) On the estimation of error-correcting parameters. In: Proceedings 15th international conference on pattern recognition, 2000, vol 2, pp 883–886Fernández D, Lladós J, Fornés A (2011) Handwritten word spotting in old manuscript images using a pseudo-structural descriptor organized in a hash structure. In: Vitri'a J, Sanches JM, Hern'andez M (eds) Pattern recognition and image analysis: Proceedings of 5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8–10. 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Contextual Word Spotting in Historical Handwritten Documents

There are countless collections of historical documents in archives and libraries that contain plenty of valuable information for historians and researchers. The extraction of this information has become a central task among the Document Analysis researches and practitioners. There is an increasing interest to digital preserve and provide access to these kind of documents. But only the digitalization is not enough for the researchers. The extraction and/or indexation of information of this documents has had an increased interest among researchers. In many cases, and in particular in historical manuscripts, the full transcription of these documents is extremely difficult due the inherent deficiencies: poor physical preservation, different writing styles, obsolete languages, etc.Word spotting has become a popular an efficient alternative to full transcription. It inherently involves a high level of degradation in the images. The search of words is holistically formulated as a visual search of a given query shape in a larger image, instead of recognising the input text and searching the query word with an ascii string comparison. But the performance of classical word spotting approaches depend on the degradation level of the images being unacceptable in many cases . In this thesis we have proposed a novel paradigm called contextual word spotting method that uses the contextual/semantic information to achieve acceptable results whereas classical word spotting does not reach.The contextual word spotting framework proposed in this thesis is a segmentation-based word spotting approach, so an efficient word segmentation is needed. Historical handwritten documents present some common difficulties that can increase the difficulties the extraction of the words. We have proposed a line segmentation approach that formulates the problem as finding the central part path in the area between two consecutive lines. This is solved as a graph traversal problem. A path finding algorithm is used to find the optimal path in a graph, previously computed, between the text lines. Once the text lines are extracted, words are localized inside the text lines using a word segmentation technique from the state of the art.Classical word spotting approaches can be improved using the contextual information of the documents. We have introduced a new framework, oriented to handwritten documents that present a highly structure, to extract information making use of context. The framework is an efficient tool for semi-automatic transcription that uses the contextual information to achieve better results than classical word spotting approaches. The contextual information is automatically discovered by recognizing repetitive structures and categorizing all the words according to semantic classes. The most frequent words in each semantic cluster are extracted and the same text is used to transcribe all them.The experimental results achieved in this thesis outperform classical word spotting approaches demonstrating the suitability of the proposed ensemble architecture for spotting words in historical handwritten documents using contextual information

Contextual Word Spotting in Historical Handwritten Documents

Advisor/s: Josep Lladós, Alicia Fornés. Date and location of PhD thesis defense: 14 November 2014, Autonomous University of BarcelonaThere are countless collections of historical documents in archives and libraries that contain plenty of valuable information for historians and researchers. The extraction of this information has become a central task among the Document Analysis researches and practitioners. There is an increasing interest to digital preserve and provide access to these kind of documents. But only the digitalization is not enough for the researchers. The extraction and/or indexation of information of this documents has had an increased interest among researchers. In many cases, and in particular in historical manuscripts, the full transcription of these documents is extremely difficult due the inherent deficiencies: poor physical preservation, different writing styles, obsolete languages, etc.Word spotting has become a popular an efficient alternative to full transcription. It inherently involves a high level of degradation in the images. The search of words is holistically formulated as a visual search of a given query shape in a larger image, instead of recognising the input text and searching the query word with an ascii string comparison. But the performance of classical word spotting approaches depend on the degradation level of the images being unacceptable in many cases . In this thesis we have proposed a novel paradigm called contextual word spotting method that uses the contextual/semantic information to achieve acceptable results whereas classical word spotting does not reach.The contextual word spotting framework proposed in this thesis is a segmentation-based word spotting approach, so an efficient word segmentation is needed. Historical handwritten documents present some common difficulties that can increase the difficulties the extraction of the words. We have proposed a line segmentation approach that formulates the problem as finding the central part path in the area between two consecutive lines. This is solved as a graph traversal problem. A path finding algorithm is used to find the optimal path in a graph, previously computed, between the text lines. Once the text lines are extracted, words are localized inside the text lines using a word segmentation technique from the state of the art.Classical word spotting approaches can be improved using the contextual information of the documents. We have introduced a new framework, oriented to handwritten documents that present a highly structure, to extract information making use of context. The framework is an efficient tool for semi-automatic transcription that uses the contextual information to achieve better results than classical word spotting approaches. The contextual information is automatically discovered by recognizing repetitive structures and categorizing all the words according to semantic classes. The most frequent words in each semantic cluster are extracted and the same text is used to transcribe all them.The experimental results achieved in this thesis outperform classical word spotting approaches demonstrating the suitability of the proposed ensemble architecture for spotting words in historical handwritten documents using contextual information

Query by String word spotting based on character bi-gram indexing

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 datasetsComment: To be published in ICDAR201

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3