HMM word graph based keyword spotting in handwritten document images

[EN] Line-level keyword spotting (KWS) is presented on the basis of frame-level word posterior probabilities. These posteriors are obtained using word graphs derived from the recogni- tion process of a full-fledged handwritten text recognizer based on hidden Markov models and N-gram language models. This approach has several advantages. First, since it uses a holistic, segmentation-free technology, it does not require any kind of word or charac- ter segmentation. Second, the use of language models allows the context of each spotted word to be taken into account, thereby considerably increasing KWS accuracy. And third, the proposed KWS scores are based on true posterior probabilities, taking into account all (or most) possible word segmentations of the input image. These scores are properly bounded and normalized. This mathematically clean formulation lends itself to smooth, threshold-based keyword queries which, in turn, permit comfortable trade-offs between search precision and recall. Experiments are carried out on several historic collections of handwritten text images, as well as a well-known data set of modern English handwrit- ten text. According to the empirical results, the proposed approach achieves KWS results comparable to those obtained with the recently-introduced "BLSTM neural networks KWS" approach and clearly outperform the popular, state-of-the-art "Filler HMM" KWS method. Overall, the results clearly support all the above-claimed advantages of the proposed ap- proach.This work has been partially supported 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).Toselli, AH.; Vidal, E.; Romero, V.; Frinken, V. (2016). HMM word graph based keyword spotting in handwritten document images. Information Sciences. 370:497-518. https://doi.org/10.1016/j.ins.2016.07.063S49751837

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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Keyword spotting for cursive document retrieval

We present one of the first attempts towards automatic retrieval of documents, in the noisy environment of unconstrained, multiple author handwritten forms. The documents were written in cursive script for which conventional OCR and text retrieval engines are not adequate. We focus on a visual word spotting indexing scheme for scanned documents housed in the Archives of the Indies in Seville, Spain. The framework presented utilizes pattern recognition, learning and information fusion methods, and is motivated from human word-spotting studies. The proposed system is described and initial results are presented

Search and information extraction in handwritten tables

[ES] Actualmente, archivos de todo el mundo están digitalizando grandes colecciones de documentos manuscritos con el fin de preservarlos y facilitar su difusión a investigadores y usuarios generales. Este hecho está motivando una gran evolución en las técnicas de reconocimiento de texto manuscrito (HTR por sus siglas en inglés), que permiten acceder a los contenidos textuales de las imágenes digitales mediante consultas de texto plano, de la misma manera que se hace con los libros y otros documentos digitales. Dentro del conjunto de documentos manuscritos sin transcripción, nos encontramos con que aproximadamente más de la mitad de los documentos se corresponden con documentos estructurados. Estos documentos contienen información de todo tipo: registros de nacimiento, de navegación, cuadernos de bitácora, etc. Toda esta información es a menudo imprescindible para usos jurídicos, estudios demográficos, estudios de la evolución del clima, etc. El objetivo de este trabajo es desarrollar nuevos métodos que permitan realizar búsquedas según el modelo "atributo-valor'" sobre estos documentos, donde los "atributos" son las cabeceras de las columnas y filas que forman la tabla y los "valores" son el resto de celdas de la tabla que no son cabecera. Para ello, vamos a basarnos en el marco de la indexación probabilistica (que está en cierto modo relacionado con el campo conocido como "keyword spotting"). En este marco, cada elemento de una imagen que se pueda interpretar como una palabra es detectado y almacenado, junto con su posición dentro de la imagen y la correspondiente probabilidad de relevancia. Así pues, empleando la información geométrica de los índices probabilísticos en conjunto con el uso de distribuciones gausianas, se pretende permitir realizar este tipo de búsquedas desde una perspectiva completamente probabilística. Bajo este enfoque, además de la búsqueda, se estudia la extracción de la información con objetivo de volcar contenidos específicos de las imágenes digitales a un formato compatible con bases de datos convencionales. En ambas tareas se han logrado resultados que superan el baseline propuesto.[EN] Currently, all archives around the world are digitising large collections of manuscripts, aiming to preserve and facilitate their dissemination to researchers and general users. This fact is motivating a fast evolution in handwritten text recognition (HTR) techniques, which allow accessing to the textual contents of digital images by means of plain-text queries, in the same way as with books and other digital documents. Among the huge set of manuscripts without transcription, more than half of the documents contain structured text. This is the case of birth records, navigation logs, etc. The information contained in these documents is often needed for legal matters, demographic studies, weather evolution studies, etc. The purpose of this work is to develop new methods that allow to perform searches according to the "attribute-value" model about these documents, where the "attributes" are, for example, column or row headers in tables and the "values" are the corresponding table cells. For this purpose, we will rely on the so-called probabilistic indexing framework (which in a certain sense is related with the field known as "keyword spotting"). In this framework, each element of an image that can be interpreted as a word is detected and stored, along with its position within the image and the correspondence relevance probability. This way, by using the geometric information available in the probabilistic indices and Gaussian distributions, we aim at allowing this type of search from a completely probabilistic perspective. Following this approach, in addition to information search, we study how to actually extract specific textual contents of the digital images in standard formats compatible with conventional databases.Andrés Moreno, J. (2021). Search and information extraction in handwritten tables. Universitat Politècnica de València. http://hdl.handle.net/10251/172740TFG

Two Methods to Improve Confidence Scores for Lexicon-Free Word Spotting in Handwritten Text

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Two methods are presented to improve word confidence scores for Line-Level Query-by-String Lexicon-Free Keyword Spotting (KWS) in handwritten text images. The first one approaches true relevance probabilities by means of computations directly carried out on character lattices obtained from the lines images considered. The second method uses the same character lattices, but it obtains relevance scores by first computing frame-level character sequence scores which resemble the word posteriorgrams used in previous approaches for lexicon-based KWS. The first method results from a formal probabilistic derivation, which allow us to better understand and further develop the underlying ideas. The second one is less formal but, according with experiments presented in the paper, it obtains almost identical results with much lower computational cost. Moreover, in contrast with the first method, the second one allows to directly obtain accurate bounding boxes for the spotted words.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 ALMAMATER, and through the EU projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).Toselli, AH.; Puigcerver, J.; Vidal, E. (2016). Two Methods to Improve Confidence Scores for Lexicon-Free Word Spotting in Handwritten Text. IEEE. https://doi.org/10.1109/ICFHR.2016.0072

Advances in Handwritten Keyword Indexing and Search Technologies

Many extensive manuscript collections are available in archives and libraries all over the world, but their textual contents remain practically inaccessible, buried under thousands of terabytes worth of high-resolution images. If perfect or sufficiently accurate text-image transcripts were available, textual content could be indexed directly for plaintext access using conventional information retrieval systems. But the results of fully automated transcriptions generally lack the level of accuracy needed for reliable text indexing and search purposes. Additionally, manual or even computer-assited transcription is entierely unsustainable when dealing with the extensive image collections typically considered for indexing. This paper explains how accurate indexing and search commands can be implemented directly on the digital images themselves without the need to explicitly resort to image transcripts. Results obtained using the proposed techniques on several relevant historical data sets are presented, clearly supporting the considerable potential of these technologies