ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016)

© 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] The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in turn were split into two distinct challenges, namely, a segmentation-based and a segmentation-free to accommodate different perspectives adopted by researchers in the KWS field. In addition, the competition aims to evaluate the submitted training-based methods under different amounts of training data. Four participants submitted at least one solution to one of the challenges, according to the capabilities and/or restrictions of their systems. The data used in the competition consisted of historical German and English documents with their own characteristics and complexities. This paper presents the details of the competition, including the data, evaluation metrics and results of the best run of each participating methods.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).Pratikakis, I.; Zagoris, K.; Gatos, B.; Puigcerver, J.; Toselli, AH.; Vidal, E. (2016). ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016). IEEE. https://doi.org/10.1109/ICFHR.2016.0117

Keyword spotting in historical handwritten documents based on graph matching

In the last decades historical handwritten documents have become increasingly available in digital form. Yet, the accessibility to these documents with respect to browsing and searching remained limited as full automatic transcription is often not possible or not sufficiently accurate. This paper proposes a novel reliable approach for template-based keyword spotting in historical handwritten documents. In particular, our framework makes use of different graph representations for segmented word images and a sophisticated matching procedure. Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting.The Hasler Foundation Switzerlandhttp://www.elsevier.com/locate/patcog2019-09-01hj2018Informatic

Probabilistic multi-word spotting in handwritten text images

[EN] Keyword spotting techniques are becoming cost-effective solutions for information retrieval in handwritten documents. We explore the extension of the single-word, line-level probabilistic indexing approach described in our previous works to allow for page-level search of queries consisting in Boolean combinations of several single-keywords. We propose heuristic rules to combine the single-word relevance probabilities into probabilistically consistent confidence scores of the multi-word boolean combinations. An empirical study, also presented in this paper, evaluates the search performance of word-pair queries involving AND and OR Boolean operations. Results of this study support the proposed approach and clearly show its effectiveness. Finally, a web-based demonstration system based on the proposed methods is presented.This work was partially supported by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMAMATER, Spanish MEC under Grant FPU13/06281, 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.; Puigcerver, J.; Noya-García, E. (2019). Probabilistic multi-word spotting in handwritten text images. Pattern Analysis and Applications. 22(1):23-32. https://doi.org/10.1007/s10044-018-0742-zS2332221Andreu Sanchez J, Romero V, Toselli A, Vidal E (2014) ICFHR2014 competition on handwritten text recognition on transcriptorium datasets (HTRtS). In: 14th International conference on frontiers in handwriting recognition (ICFHR), 2014, pp 785–790Bazzi I, Schwartz R, Makhoul J (1999) An omnifont open-vocabulary OCR system for English and Arabic. 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In: First Proceedings of international workshop on document image analysis for libraries, 2004, pp 278–287. https://doi.org/10.1109/DIAL.2004.1263256Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval. Cambridge University Press, New YorkMarti UV, Bunke H (2002) The iam-database: an english sentence database for offline handwriting recognition. Int J Doc Anal Recogn 5:39–46. https://doi.org/10.1007/s100320200071Noya-García E, Toselli AH, Vidal E (2017) Simple and effective multi-word query spotting in handwritten text images, pp 76–84. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-58838-4_9Pratikakis I, Zagoris K, Gatos B, Louloudis G, Stamatopoulos N (2014) ICFHR 2014 competition on handwritten keyword spotting (h-kws 2014). In: 14th International conference on frontiers in handwriting recognition (ICFHR), 2014, pp 814–819Puigcerver J, Toselli AH, Vidal E (2015) Icdar2015 competition on keyword spotting for handwritten documents. In: 13th international conference on document analysis and recognition (ICDAR), 2015, pp 1176–1180Riba P, Almazn J, Forns A, Fernndez-Mota D, Valveny E, Llads J (2014) e-crowds: a mobile platform for browsing and searching in historical demography-related manuscripts. In: 14th International conference on frontiers in handwriting recognition (ICFHR), 2014, pp 228–233. https://doi.org/10.1109/ICFHR.2014.46Robertson S (2008) A new interpretation of average precision. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval (SIGIR ’08), pp 689–690. ACM, New York. https://doi.org/10.1145/1390334.1390453Romero V, Toselli AH, Vidal E (2012) Multimodal interactive handwritten text transcription. Series in machine perception and artificial intelligence (MPAI). World Scientific Publishing, SingaporeSánchez JA, Romero V, Toselli AH, Vidal E (2016) ICFHR2016 competition on handwritten text recognition on the READ dataset. In: 15th International conference on frontiers in handwriting recognition (ICFHR’16), pp 630–635. https://doi.org/10.1109/ICFHR.2016.0120Toselli A, Vidal E (2015) Handwritten text recognition results on the Bentham collection with improved classical N-Gram-HMM methods. In: 3rd International workshop on historical document imaging and processing (HIP15), pp 15–22Toselli AH, Juan A, Keysers D, González J, Salvador I, Ney H, Vidal E, Casacuberta F (2004) Integrated Handwriting Recognition and Interpretation using Finite-State Models. Int J Pattern Recogn Artif Intell 18(4):519–539Toselli AH, Vidal E, Romero V, Frinken V (2016) HMM word graph based keyword spotting in handwritten document images. 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Working paper 2004-09 Department of Statistics and Actuarial Science–University of Waterlo

Spotting Keywords in Offline Handwritten Documents Using Hausdorff Edit Distance

Keyword spotting has become a crucial topic in handwritten document recognition, by enabling content-based retrieval of scanned documents using search terms. With a query keyword, one can search and index the digitized handwriting which in turn facilitates understanding of manuscripts. Common automated techniques address the keyword spotting problem through statistical representations. Structural representations such as graphs apprehend the complex structure of handwriting. However, they are rarely used, particularly for keyword spotting techniques, due to high computational costs. The graph edit distance, a powerful and versatile method for matching any type of labeled graph, has exponential time complexity to calculate the similarities of graphs. Hence, the use of graph edit distance is constrained to small size graphs. The recently developed Hausdorff edit distance algorithm approximates the graph edit distance with quadratic time complexity by efficiently matching local substructures. This dissertation speculates using Hausdorff edit distance could be a promising alternative to other template-based keyword spotting approaches in term of computational time and accuracy. Accordingly, the core contribution of this thesis is investigation and development of a graph-based keyword spotting technique based on the Hausdorff edit distance algorithm. The high representational power of graphs combined with the efficiency of the Hausdorff edit distance for graph matching achieves remarkable speedup as well as accuracy. In a comprehensive experimental evaluation, we demonstrate the solid performance of the proposed graph-based method when compared with state of the art, both, concerning precision and speed. The second contribution of this thesis is a keyword spotting technique which incorporates dynamic time warping and Hausdorff edit distance approaches. The structural representation of graph-based approach combined with statistical geometric features representation compliments each other in order to provide a more accurate system. The proposed system has been extensively evaluated with four types of handwriting graphs and geometric features vectors on benchmark datasets. The experiments demonstrate a performance boost in which outperforms individual systems