Some variants of Hausdorff distance for word matching

Several recently proposed modifications of Hausdorff distance (HD) are examined with respect to word image matching for bad quality typewritten Bulgarian text. The main idea of these approaches presumes that omission of the extreme distances between the points of the compared images eliminates the noise (to some extent) and the algorithms become more robust. A few robust HD measures, namely, censored HD, LTS-HD, and a new binary image comparison method that uses a windowed Hausdorff distance, lie in the base of the computer experiments carried out using 54 pages of typewritten text

Handwritten word-image retrieval with synthesized typed queries

We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained “on the fly ” with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram (LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models (SC-HMM), in which the typed-text models are constrained to a “vocabulary ” of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SC-HMM is crucial. To the best of the authors ’ knowledge, this is the first work to address this issue in a non-trivial manner

Hausdorff distances for searching in binary text images

Hausdorff distance (HD) seems the most efficient instrument for measuring how far two compact non-empty subsets of a metric space are from each other. This paper considers the possibilities provided by HD and some of its modifications used recently by many authors for resemblance between binary text images. Summarizing part of the existing word image matching methods, relied on HD, we investigate a new similar parameterized method which contains almost all of them as particular cases. Numerical experiments for searching words in binary text images are carried out with 333 pages of old Bulgarian typewritten text, 200 printed pages of Bulgarian Chrestomathy from year 1884, and 200 handwritten pages of Slavonic manuscript from year 1574. They outline how the parameters must be set in order to use the advantages of the proposed method for the purposes of word matching in scanned document images

Keyword-guided word spotting in historical printed documents using synthetic data and user feedback

In this paper, we propose a novel technique for word spotting in historical printed documents combining synthetic data and user feedback. Our aim is to search for keywords typed by the user in a large collection of digitized printed historical documents. The proposed method consists of the following stages: (1) creation of synthetic image words; (2) word segmentation using dynamic parameters; (3) efficient feature extraction for each word image and (4) a retrieval procedure that is optimized by user feedback. Experimental results prove the efficiency of the proposed approach. © Springer-Verlag 2007

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. Springer, Berlin, Heidelberg, pp 628–635. doi: 10.1007/978-3-642-21257-4_78Fischer A, Keller A, Frinken V, Bunke H (2012) Lexicon-free handwritten word spotting using character HMMs. Pattern Recognit Lett 33(7):934–942. doi: 10.1016/j.patrec.2011.09.009 Special Issue on Awards from ICPR 2010Fornés A, Frinken V, Fischer A, Almazán J, Jackson G, Bunke H (2011) A keyword spotting approach using blurred shape model-based descriptors. In: Proceedings of the 2011 workshop on historical document imaging and processing, pp 83–90. ACMFrinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE Trans Pattern Anal Mach Intell 34(2):211–224. doi: 10.1109/TPAMI.2011.113Gatos B, Pratikakis I (2009) Segmentation-free word spotting in historical printed documents. In: 10th International conference on document analysis and recognition, 2009. ICDAR’09, pp 271–275. IEEEJelinek F (1998) Statistical methods for speech recognition. MIT Press, CambridgeKneser R, Ney H (1995) Improved backing-off for N-gram language modeling. In: International conference on acoustics, speech and signal processing (ICASSP ’95), vol 1, pp 181–184. IEEE Computer Society, Los Alamitos, CA, USA. doi: http://doi.ieeecomputersociety.org/10.1109/ICASSP.1995.479394Kolcz A, Alspector J, Augusteijn M, Carlson R, Popescu GV (2000) A line-oriented approach to word spotting in handwritten documents. Pattern Anal Appl 3:153–168. doi: 10.1007/s100440070020Konidaris T, Gatos B, Ntzios K, Pratikakis I, Theodoridis S, Perantonis SJ (2007) Keyword-guided word spotting in historical printed documents using synthetic data and user feedback. Int J Doc Anal Recognit 9(2–4):167–177Kumar G, Govindaraju V (2014) Bayesian active learning for keyword spotting in handwritten documents. In: 2014 22nd International conference on pattern recognition (ICPR), pp 2041–2046. IEEELevenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Dokl 10(8):707–710Manning CD, Raghavan P, Schtze 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 Recognit 5(1):39–46. doi: 10.1007/s100320200071Puigcerver J, Toselli AH, Vidal E (2014) Word-graph and character-lattice combination for KWS in handwritten documents. In: 14th International conference on frontiers in handwriting recognition (ICFHR), pp 181–186Puigcerver J, Toselli AH, Vidal E (2014) Word-graph-based handwriting keyword spotting of out-of-vocabulary queries. In: 22nd International conference on pattern recognition (ICPR), pp 2035–2040Puigcerver J, Toselli AH, Vidal E (2015) A new smoothing method for lexicon-based handwritten text keyword spotting. In: 7th Iberian conference on pattern recognition and image analysis. SpringerRath T, Manmatha R (2007) Word spotting for historical documents. Int J Doc Anal Recognit 9:139–152Robertson 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, NY, USA. doi: http://doi.acm.org/10.1145/1390334.1390453Rodriguez-Serrano JA, Perronnin F (2009) Handwritten word-spotting using hidden markov models and universal vocabularies. Pattern Recognit 42(9):2106–2116. doi: 10.1016/j.patcog.2009.02.005 . http://www.sciencedirect.com/science/article/pii/S0031320309000673Rusinol M, Aldavert D, Toledo R, Llados J (2011) Browsing heterogeneous document collections by a segmentation-free word spotting method. In: International conference on document analysis and recognition (ICDAR), pp 63–67. doi: 10.1109/ICDAR.2011.22Shang H, Merrettal T (1996) Tries for approximate string matching. IEEE Trans Knowl Data Eng 8(4):540–547Toselli AH, Vidal E (2013) Fast HMM-Filler approach for key word spotting in handwritten documents. In: Proceedings of the 12th international conference on document analysis and recognition (ICDAR), pp 501–505Toselli AH, Vidal E (2014) Word-graph based handwriting key-word spotting: impact of word-graph size on performance. In: 11th IAPR international workshop on document analysis systems (DAS), pp 176–180. IEEEToselli AH, Vidal E, Romero V, Frinken V (2013) Word-graph based keyword spotting and indexing of handwritten document images. Technical report, Universitat Politécnica de ValénciaVidal E, Toselli AH, Puigcerver J (2015) High performance query-by-example keyword spotting using query-by-string techniques. In: 2015 13th International conference on document analysis and recognition (ICDAR), pp 741–745. IEEEWoodland P, Leggetter C, Odell J, Valtchev V, Young S (1995) The 1994 HTK large vocabulary speech recognition system. In: International conference on acoustics, speech, and signal processing (ICASSP ’95), vol 1, pp 73 –76. doi: 10.1109/ICASSP.1995.479276Wshah S, Kumar G, Govindaraju V (2012) Script independent word spotting in offline handwritten documents based on hidden markov models. In: 2012 International conference on frontiers in handwriting recognition (ICFHR), pp 14–19. doi: 10.1109/ICFHR.2012.26