READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents

Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.Comment: Submitted to DAS201

On-the-fly Historical Handwritten Text Annotation

The performance of information retrieval algorithms depends upon the availability of ground truth labels annotated by experts. This is an important prerequisite, and difficulties arise when the annotated ground truth labels are incorrect or incomplete due to high levels of degradation. To address this problem, this paper presents a simple method to perform on-the-fly annotation of degraded historical handwritten text in ancient manuscripts. The proposed method aims at quick generation of ground truth and correction of inaccurate annotations such that the bounding box perfectly encapsulates the word, and contains no added noise from the background or surroundings. This method will potentially be of help to historians and researchers in generating and correcting word labels in a document dynamically. The effectiveness of the annotation method is empirically evaluated on an archival manuscript collection from well-known publicly available datasets

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

ICFHR2016 Competition on Handwritten Text Recognition on the READ Dataset

© 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] This paper describes the Handwritten Text Recognition (HTR) competition on the READ dataset that has been held in the context of the International Conference on Frontiers in Handwriting Recognition 2016. This competition aims to bring together researchers working on off-line HTR and provide them a suitable benchmark to compare their techniques on the task of transcribing typical historical handwritten documents. Two tracks with different conditions on the use of training data were proposed. Ten research groups registered in the competition but finally five submitted results. The handwritten images for this competition were drawn from the German document Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805, used in the READ project. The selected dataset is written by several hands and entails significant variabilities and difficulties. The five participants achieved good results with transcriptions word error rates ranging from 21% to 47% and character error rates rating from 5% to 19%.This work has been partially supported through the European Union's H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943), and the MINECO/FEDER UE project TIN2015-70924-C2-1-R.Sánchez Peiró, JA.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2016). ICFHR2016 Competition on Handwritten Text Recognition on the READ Dataset. IEEE. https://doi.org/10.1109/ICFHR.2016.0120

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. IEEE Trans Pattern Anal Mach Intell 21(6):495–504Bluche T, Hamel S, Kermorvant C, Puigcerver J, Stutzmann D, Toselli AH, Vidal E (2017) Preparatory KWS experiments for large-scale indexing of a vast medieval manuscript collection in the hIMANIS Project. In: 14th International conference on document analysis and recognition (ICDAR). (Accepted)Bluche T, Hamel S, Kermorvant C, Puigcerver J, Stutzmann D, Toselli AH, Vidal E (2017) Preparatory kws experiments for large-scale indexing of a vast medieval manuscript collection in the himanis project. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol. 01, pp 311–316. https://doi.org/10.1109/ICDAR.2017.59Boole G (1854) An investigation of the laws of thought on which are founded the mathematical theories of logic and probabilities. Macmillan, New YorkCauser T, Wallace V (2012) Building a volunteer community: results and findings from Transcribe Bentham. Digital Humanities Quarterly 6España-Boquera S, Castro-Bleda MJ, Gorbe-Moya J, Zamora-Martinez F (2011) Improving offline handwritten text recognition with hybrid hmm/ann models. IEEE Trans Pattern Anal Mach Intell 33(4):767–779. https://doi.org/10.1109/TPAMI.2010.141Fischer A, Wuthrich M, Liwicki M, Frinken V, Bunke H, Viehhauser G, Stolz M (2009) Automatic transcription of handwritten medieval documents. In: 15th International conference on virtual systems and multimedia, 2009. VSMM ’09, pp 137–142. https://doi.org/10.1109/VSMM.2009.26Fréchet M (1935) Généralisations du théorème des probabilités totales. Seminarjum MatematyczneFréchet M (1951) Sur les tableaux de corrélation dont les marges sont données. Ann Univ Lyon 3 $$^{\wedge }$$ ∧ e ser Sci Sect A 14:53–77Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J (2009) A novel connectionist system for unconstrained handwriting recognition. IEEE Trans Pattern Anal Mach Intell 31(5):855–868Jelinek 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), IEEE Computer Society, Los Alamitos, vol. 1, pp. 181–184, https://doi.org/10.1109/ICASSP.1995.479394Kozielski M, Forster J, Ney H (2012) Moment-based image normalization for handwritten text recognition. In: Proceedings of the 2012 international conference on frontiers in handwriting recognition, ICFHR ’12, pp 256–261. IEEE Computer Society, Washington. https://doi.org/10.1109/ICFHR.2012.236Lavrenko V, Rath TM, Manmatha R (2004) Holistic word recognition for handwritten historical documents. 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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Transforming scholarship in the archives through handwritten text recognition:Transkribus as a case study

Purpose: An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. - Design/methodology/approach: This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. - Findings: Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. - Research limitations/implications: The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. - Practical implications: Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. - Social implications: The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. - Originality/value: This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector

A Set of Benchmarks for Handwritten Text Recognition on Historical Documents

[EN] Handwritten Text Recognition is a important requirement in order to make visible the contents of the myriads of historical documents residing in public and private archives and libraries world wide. Automatic Handwritten Text Recognition (HTR) is a challenging problem that requires a careful combination of several advanced Pattern Recognition techniques, including but not limited to Image Processing, Document Image Analysis, Feature Extraction, Neural Network approaches and Language Modeling. The progress of this kind of systems is strongly bound by the availability of adequate benchmarking datasets, software tools and reproducible results achieved using the corresponding tools and datasets. Based on English and German historical documents proposed in recent open competitions at ICDAR and ICFHR conferences between 2014 and 2017, this paper introduces four HTR benchmarks in order of increasing complexity from several points of view. For each benchmark, a specific system is proposed which overcomes results published so far under comparable conditions. Therefore, this paper establishes new state of the art baseline systems and results which aim at becoming new challenges that would hopefully drive further improvement of HTR technologies. Both the datasets and the software tools used to implement the baseline systems are made freely accessible for research purposes. (C) 2019 Elsevier Ltd. All rights reserved.This work has been partially supported through the European Union's H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943), as well as by the BBVA Foundation through the 2017-2018 and 2018-2019 Digital Humanities research grants "Carabela" and "HisClima - Dos Siglos de Datos Cilmaticos", and by EU JPICH project "HOME - History Of Medieval Europe" (Spanish PEICTI Ref. PC12018-093122).Sánchez Peiró, JA.; Romero, V.; Toselli, AH.; Villegas, M.; Vidal, E. (2019). A Set of Benchmarks for Handwritten Text Recognition on Historical Documents. Pattern Recognition. 94:122-134. https://doi.org/10.1016/j.patcog.2019.05.025S1221349