Transcribing a 17th-century botanical manuscript: Longitudinal evaluation of document layout detection and interactive transcription

[EN] We present a process for cost-effective transcription of cursive handwritten text images that has been tested on a 1,000-page 17th-century book about botanical species. The process comprised two main tasks, namely: (1) preprocessing: page layout analysis, text line detection, and extraction; and (2) transcription of the extracted text line images. Both tasks were carried out with semiautomatic pro- cedures, aimed at incrementally minimizing user correction effort, by means of computer-assisted line detection and interactive handwritten text recognition technologies. The contribution derived from this work is three-fold. First, we provide a detailed human-supervised transcription of a relatively large historical handwritten book, ready to be searchable, indexable, and accessible to cultural heritage scholars as well as the general public. Second, we have conducted the first longitudinal study to date on interactive handwriting text recognition, for which we provide a very comprehensive user assessment of the real-world per- formance of the technologies involved in this work. Third, as a result of this process, we have produced a detailed transcription and document layout infor- mation (i.e. high-quality labeled data) ready to be used by researchers working on automated technologies for document analysis and recognition.This work is supported by the European Commission through the EU projects HIMANIS (JPICH program, Spanish, grant Ref. PCIN-2015-068) and READ (Horizon-2020 program, grant Ref. 674943); and the Universitat Politecnica de Valencia (grant number SP20130189). This work was also part of the Valorization and I+D+i Resources program of VLC/CAMPUS and has been funded by the Spanish MECD as part of the International Excellence Campus program.Toselli, AH.; Leiva, LA.; Bordes-Cabrera, I.; Hernández-Tornero, C.; Bosch Campos, V.; Vidal, E. (2018). Transcribing a 17th-century botanical manuscript: Longitudinal evaluation of document layout detection and interactive transcription. Digital Scholarship in the Humanities. 33(1):173-202. https://doi.org/10.1093/llc/fqw064S173202331Bazzi, I., Schwartz, R., & Makhoul, J. (1999). An omnifont open-vocabulary OCR system for English and Arabic. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(6), 495-504. doi:10.1109/34.771314Causer, T., Tonra, J., & Wallace, V. (2012). Transcription maximized; expense minimized? Crowdsourcing and editing The Collected Works of Jeremy Bentham*. Literary and Linguistic Computing, 27(2), 119-137. doi:10.1093/llc/fqs004Ramel, J. Y., Leriche, S., Demonet, M. L., & Busson, S. (2007). User-driven page layout analysis of historical printed books. International Journal of Document Analysis and Recognition (IJDAR), 9(2-4), 243-261. doi:10.1007/s10032-007-0040-6Romero, V., Fornés, A., Serrano, N., Sánchez, J. A., Toselli, A. H., Frinken, V., … Lladós, J. (2013). The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recognition, 46(6), 1658-1669. doi:10.1016/j.patcog.2012.11.024Romero, V., Toselli, A. H., & Vidal, E. (2012). Multimodal Interactive Handwritten Text Transcription. Series in Machine Perception and Artificial Intelligence. doi:10.1142/8394Toselli, A. H., Romero, V., Pastor, M., & Vidal, E. (2010). Multimodal interactive transcription of text images. Pattern Recognition, 43(5), 1814-1825. doi:10.1016/j.patcog.2009.11.019Toselli, A. H., Vidal, E., Romero, V., & Frinken, V. (2016). HMM word graph based keyword spotting in handwritten document images. Information Sciences, 370-371, 497-518. doi:10.1016/j.ins.2016.07.063Bunke, H., Bengio, S., & Vinciarelli, A. (2004). Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 709-720. doi:10.1109/tpami.2004.1

Transcription of the Bleek and Lloyd Collection using the Bossa Volunteer Thinking Framework

The digital Bleek and Lloyd Collection is a rare collection that contains artwork, notebooks and dictionaries of the earliest habitants of Southern Africa. Previous attempts have been made to recognize the complex text in the notebooks using machine learning techniques, but due to the complexity of the manuscripts the recognition accuracy was low. In this research, a crowdsourcing based method is proposed to transcribe the historical handwritten manuscripts, where volunteers transcribe the notebooks online. An online crowdsourcing transcription tool was developed and deployed. Experiments were conducted to determine the quality of transcriptions and accuracy of the volunteers compared with a gold standard. The results show that volunteers are able to produce reliable transcriptions of high quality. The inter-transcriber agreement is 80% for |Xam text and 95% for English text. When the |Xam text transcriptions produced by the volunteers are compared with the gold standard, the volunteers achieve an average accuracy of 69.69%. Findings show that there exists a positive linear correlation between the inter-transcriber agreement and the accuracy of transcriptions. The user survey revealed that volunteers found the transcription process enjoyable, though it was difficult. Results indicate that volunteer thinking can be used to crowdsource intellectually-intensive tasks in digital libraries like transcription of handwritten manuscripts. Volunteer thinking outperforms machine learning techniques at the task of transcribing notebooks from the Bleek and Lloyd Collection

Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. On Multimodal Interactive Machine Translation Using Speech Recognition. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2011a.Alabau V. Sanchis A. Casacuberta F. Improving On-Line Handwritten Recognition using Translation Models in Multimodal Interactive Machine Translation. Proc. Assoc. Comput. Linguistics (ACL) 2011b.Alabau, V., Sanchis, A., & Casacuberta, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition, 47(3), 1217-1228. doi:10.1016/j.patcog.2013.09.035Anthony L. Wobbrock J. O. A Lightweight Multistroke Recognizer for User Interface Prototypes. Proc. Conf. Graph. Interface (GI). 2010.Anthony L. Wobbrock J. O. N-Protractor: a Fast and Accurate Multistroke Recognizer. Proc. Conf. Graph. Interface (GI) 2012.Anthony L. Vatavu R.-D. Wobbrock J. O. Understanding the Consistency of Users' Pen and Finger Stroke Gesture Articulation. Proc. Conf. Graph. Interface (GI). 2013.Appert C. Zhai S. 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Interactive Error Repair for an Online Handwriting Interface. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Jelinek F. Cambridge, Massachusetts: MIT Press; 1998. Statistical Methods for Speech Recognition.Johansson S. Atwell E. Garside R. Leech G. The Tagged LOB Corpus, User's Manual. Norwegian Computing Center for the Humanities. 1996.Karat C.-M. Halverson C. Horn D. Karat J. Patterns of Entry and Correction in Large Vocabulary Continuous Speech Recognition Systems. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 1999.Kerrick, D. D., & Bovik, A. C. (1988). Microprocessor-based recognition of handprinted characters from a tablet input. Pattern Recognition, 21(5), 525-537. doi:10.1016/0031-3203(88)90011-8Koschinski M. Winkler H. Lang M. Segmentation and Recognition of Symbols within Handwritten Mathematical Expressions. Proc. IEEE Int. Conf. Acoustics Speech Signal Process. (ICASSP). 1995.Kosmala A. Rigoll G. On-Line Handwritten Formula Recognition Using Statistical Methods. Proc. Int. Conf. Pattern Recognition (ICPR) 1998.Kristensson P. O. Discrete and continuous shape writing for text entry and control. 2007. Ph.D. Thesis, Linköping University, Sweden.Kristensson P. O. Denby L. C. Text Entry Performance of State of the Art Unconstrained Handwriting Recognition: a Longitudinal User Study. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2009.Kristensson P. O. Denby L. C. Continuous Recognition and Visualization of Pen Strokes and Touch-Screen Gestures. Proc. Eighth Eurograph. Symp. Sketch-Based Interfaces Model. (SBIM) 2011.Kristensson P. O. Zhai S. SHARK2: A Large Vocabulary Shorthand Writing System for Pen-Based Computers. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 2004.Kurtenbach G. P. The design and evaluation of marking menus. 1991. Ph.D. Thesis, University of Toronto.Kurtenbach G. P. Buxton W. Issues in Combining Marking and Direct Manipulation Techniques. Proc. ACM Symp. User Interface Softw. Technol. (UIST). 1991.Kurtenbach G. Buxton W. User Learning and Performance with Marking Menus. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 1994.Kurtenbach, G., Sellen, A., & Buxton, W. (1993). An Empirical Evaluation of Some Articulatory and Cognitive Aspects of Marking Menus. Human-Computer Interaction, 8(1), 1-23. doi:10.1207/s15327051hci0801_1LaLomia M. User Acceptance of Handwritten Recognition Accuracy. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 1994.Leiva L. A. Romero V. Toselli A. H. Vidal E. Evaluating an Interactive–Predictive Paradigm on Handwriting Transcription: A Case Study and Lessons Learned. Proc. 35th Annu. IEEE Comput. Softw. Appl. Conf. (COMPSAC) 2011.Leiva L. A. Alabau V. Vidal E. Error-Proof, High-Performance, and Context-Aware Gestures for Interactive Text Edition. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2013.Li Y. Protractor: A Fast and Accurate Gesture Recognizer. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 2010.Li W. Hammond T. Using Scribble Gestures to Enhance Editing Behaviors of Sketch Recognition Systems. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA). 2012.Liao C. Guimbretière F. Hinckley K. Hollan J. Papiercraft: a gesture-based command system for interactive paper. ACM Trans. Comput.–Hum. Interaction (TOCHI) 2008;14:18:1-18:27.Liu P. Soong F. K. Word Graph Based Speech Rcognition Error Correction by Handwriting Input. Proc. Int. Conf. Multimodal Interfaces (ICMI). 2006.Long A. Landay J. Rowe L. Implications for a Gesture Design Tool. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI) 1999.Long A. C. Jr. Landay J. A. Rowe L. A. Michiels J. Visual Similarity of Pen Gestures. Proc. SIGCHI Conf. Hum. Factors Comput. Syst. (CHI). 2000.MacKenzie, I. S., & Chang, L. (1999). A performance comparison of two handwriting recognizers. Interacting with Computers, 11(3), 283-297. doi:10.1016/s0953-5438(98)00030-7MacKenzie I. S. 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Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords

Lanthorn, vol. 46, no. 05, September 8, 2011

Lanthorn is Grand Valley State\u27s student newspaper, published from 1968 to the present

Time as told : telling the past in Kyrgyzstan

This paper addresses Kyrgyz ‘ time-telling ’, exploring how Kyrgyz herders and villagers ‘tell’ of their experience of time through genealogies, family stories and epic poetry. The author takes a phenomenological approach, drawing on different forms of narrative; interweaving history, myth and story; revealing the life within the past, as genres mesh (and not always seamlessly). She argues that the lived experience of ‘time-telling’ works through narrative, memory, sound, performance, and poetics, providing a matrix through which the past is continuously brought to life for performers and audience alike. The paper is in three parts. The first sets the scene, exploring three interwoven, kin-related Kyrgyz genres – family trees, genealogies, and epic poetry. The second looks at diverse manifestations of the Kyrgyz epic Manas, and its interpenetration with social life. The third reveals how different forms of performing and remembering the epic bring the past to life through the act of performance.PostprintPeer reviewe

Lanthorn, vol. 35, no. 32, May 17, 2001

Lanthorn is Grand Valley State\u27s student newspaper, published from 1968 to the present

Advances in deep learning with limited supervision and computational resources

Les réseaux de neurones profonds sont la pierre angulaire des systèmes à la fine pointe de la technologie pour une vaste gamme de tâches, comme la reconnaissance d'objets, la modélisation du langage et la traduction automatique. Mis à part le progrès important établi dans les architectures et les procédures de formation des réseaux de neurones profonds, deux facteurs ont été la clé du succès remarquable de l'apprentissage profond : la disponibilité de grandes quantités de données étiquetées et la puissance de calcul massive. Cette thèse par articles apporte plusieurs contributions à l'avancement de l'apprentissage profond, en particulier dans les problèmes avec très peu ou pas de données étiquetées, ou avec des ressources informatiques limitées. Le premier article aborde la question de la rareté des données dans les systèmes de recommandation, en apprenant les représentations distribuées des produits à partir des commentaires d'évaluation de produits en langage naturel. Plus précisément, nous proposons un cadre d'apprentissage multitâches dans lequel nous utilisons des méthodes basées sur les réseaux de neurones pour apprendre les représentations de produits à partir de textes de critiques de produits et de données d'évaluation. Nous démontrons que la méthode proposée peut améliorer la généralisation dans les systèmes de recommandation et atteindre une performance de pointe sur l'ensemble de données Amazon Reviews. Le deuxième article s'attaque aux défis computationnels qui existent dans l'entraînement des réseaux de neurones profonds à grande échelle. Nous proposons une nouvelle architecture de réseaux de neurones conditionnels permettant d'attribuer la capacité du réseau de façon adaptative, et donc des calculs, dans les différentes régions des entrées. Nous démontrons l'efficacité de notre modèle sur les tâches de reconnaissance visuelle où les objets d'intérêt sont localisés à la couche d'entrée, tout en maintenant une surcharge de calcul beaucoup plus faible que les architectures standards des réseaux de neurones. Le troisième article contribue au domaine de l'apprentissage non supervisé, avec l'aide du paradigme des réseaux antagoniste génératifs. Nous introduisons un cadre fléxible pour l'entraînement des réseaux antagonistes génératifs, qui non seulement assure que le générateur estime la véritable distribution des données, mais permet également au discriminateur de conserver l'information sur la densité des données à l'optimum global. Nous validons notre cadre empiriquement en montrant que le discriminateur est capable de récupérer l'énergie de la distribution des données et d'obtenir une qualité d'échantillons à la fine pointe de la technologie. Enfin, dans le quatrième article, nous nous attaquons au problème de l'apprentissage non supervisé à travers différents domaines. Nous proposons un modèle qui permet d'apprendre des transformations plusieurs à plusieurs à travers deux domaines, et ce, à partir des données non appariées. Nous validons notre approche sur plusieurs ensembles de données se rapportant à l'imagerie, et nous montrons que notre méthode peut être appliquée efficacement dans des situations d'apprentissage semi-supervisé.Deep neural networks are the cornerstone of state-of-the-art systems for a wide range of tasks, including object recognition, language modelling and machine translation. In the last decade, research in the field of deep learning has led to numerous key advances in designing novel architectures and training algorithms for neural networks. However, most success stories in deep learning heavily relied on two main factors: the availability of large amounts of labelled data and massive computational resources. This thesis by articles makes several contributions to advancing deep learning, specifically in problems with limited or no labelled data, or with constrained computational resources. The first article addresses sparsity of labelled data that emerges in the application field of recommender systems. We propose a multi-task learning framework that leverages natural language reviews in improving recommendation. Specifically, we apply neural-network-based methods for learning representations of products from review text, while learning from rating data. We demonstrate that the proposed method can achieve state-of-the-art performance on the Amazon Reviews dataset. The second article tackles computational challenges in training large-scale deep neural networks. We propose a conditional computation network architecture which can adaptively assign its capacity, and hence computations, across different regions of the input. We demonstrate the effectiveness of our model on visual recognition tasks where objects are spatially localized within the input, while maintaining much lower computational overhead than standard network architectures. The third article contributes to the domain of unsupervised learning with the generative adversarial networks paradigm. We introduce a flexible adversarial training framework, in which not only the generator converges to the true data distribution, but also the discriminator recovers the relative density of the data at the optimum. We validate our framework empirically by showing that the discriminator is able to accurately estimate the true energy of data while obtaining state-of-the-art quality of samples. Finally, in the fourth article, we address the problem of unsupervised domain translation. We propose a model which can learn flexible, many-to-many mappings across domains from unpaired data. We validate our approach on several image datasets, and we show that it can be effectively applied in semi-supervised learning settings

Lanthorn, vol. 45, no. 41, February 10, 2011

Lanthorn is Grand Valley State\u27s student newspaper, published from 1968 to the present