Automatic Palaeographic Exploration of Genizah Manuscripts

The Cairo Genizah is a collection of hand-written documents containing approximately 350,000 fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in some 75 libraries and private collections worldwide, but there is an ongoing effort to document and catalogue all extant fragments. Palaeographic information plays a key role in the study of the Genizah collection. Script style, and–more specifically–handwriting, can be used to identify fragments that might originate from the same original work. Such matched fragments, commonly referred to as “joins”, are currently identified manually by experts, and presumably only a small fraction of existing joins have been discovered to date. In this work, we show that automatic handwriting matching functions, obtained from non-specific features using a corpus of writing samples, can perform this task quite reliably. In addition, we explore the problem of grouping various Genizah documents by script style, without being provided any prior information about the relevant styles. The automatically obtained grouping agrees, for the most part, with the palaeographic taxonomy. In cases where the method fails, it is due to apparent similarities between related scripts

The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition

NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern RecognitionVolume 46, Issue 6, June 2013, Pages 1658–1669 DOI: 10.1016/j.patcog.2012.11.024[EN] Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies. © 2012 Elsevier Ltd. All rights reserved.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV ‘‘Consolider Ingenio 2010’’ program (CSD2007-00018), MITTRAL (TIN2009-14633-C03-01) and KEDIHC ((TIN2009-14633-C03-03) projects. This work has been partially supported by the European Research Council Advanced Grant (ERC-2010-AdG-20100407: 269796-5CofM) and the European seventh framework project (FP7-PEOPLE-2008-IAPP: 230653-ADAO). Also supported by the Generalitat Valenciana under grant Prometeo/2009/014 and FPU AP2007-02867, and by the Universitat Politecnica de Val encia (PAID-05-11). We would also like to thank the Center for Demographic Studies (UAB) and the Cathedral of Barcelona.Romero Gómez, V.; Fornés, A.; Serrano Martínez-Santos, N.; Sánchez Peiró, JA.; Toselli ., AH.; Frinken, V.; Vidal, E.... (2013). The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recognition. 46(6):1658-1669. https://doi.org/10.1016/j.patcog.2012.11.024S1658166946

Detecting Authorship, Hands, and Corrections in Historical Manuscripts. A Mixedmethods Approach towards the Unpublished Writings of an 18th Century Czech Emigré Community in Berlin (Handwriting)

When one starts working philologically with historical manuscripts, one faces important first questions involving authorship, writers’ hands andthe history of documenttransmission. These issues are especially thorny with documents remaining outside the established canon, such as privatemanuscripts, aboutwhichwehave very restrictedtext-externalinformation. In this area – so we argue – it is especially fruitful to employ a mixed-methods approach, combiningtailored automatic methods from image recognition/analysis with philological and linguistic knowledge.Whileimage analysis captureswriters’ hands, linguistic/philological research mainly addressestextual authorship;thetwo cross-fertilize and obtain a coherent interpretation which may then be evaluated against the available text-external historical evidence. Departingfrom our ‘lab case’,whichis a corpus of unedited Czechmanuscriptsfromthe archive of a small 18th century migrant community, the Herrnhuter Brüdergemeine (Brethren parish) in Berlin-Neukölln, our project has developed an assistance system which aids philologists in working with digitized (scanned) hand-written historical sources. We present its application and discuss its general potential and methodological implications

Interactive Transcription of Old Text Documents

Nowadays, there are huge collections of handwritten text documents in libraries all over the world. The high demand for these resources has led to the creation of digital libraries in order to facilitate the preservation and provide electronic access to these documents. However text transcription of these documents im- ages are not always available to allow users to quickly search information, or computers to process the information, search patterns or draw out statistics. The problem is that manual transcription of these documents is an expensive task from both economical and time viewpoints. This thesis presents a novel ap- proach for e cient Computer Assisted Transcription (CAT) of handwritten text documents using state-of-the-art Handwriting Text Recognition (HTR) systems. The objective of CAT approaches is to e ciently complete a transcription task through human-machine collaboration, as the e ort required to generate a manual transcription is high, and automatically generated transcriptions from state-of-the-art systems still do not reach the accuracy required. This thesis is centered on a special application of CAT, that is, the transcription of old text document when the quantity of user e ort available is limited, and thus, the entire document cannot be revised. In this approach, the objective is to generate the best possible transcription by means of the user e ort available. This thesis provides a comprehensive view of the CAT process from feature extraction to user interaction. First, a statistical approach to generalise interactive transcription is pro- posed. As its direct application is unfeasible, some assumptions are made to apply it to two di erent tasks. First, on the interactive transcription of hand- written text documents, and next, on the interactive detection of the document layout. Next, the digitisation and annotation process of two real old text documents is described. This process was carried out because of the scarcity of similar resources and the need of annotated data to thoroughly test all the developed tools and techniques in this thesis. These two documents were carefully selected to represent the general di culties that are encountered when dealing with HTR. Baseline results are presented on these two documents to settle down a benchmark with a standard HTR system. Finally, these annotated documents were made freely available to the community. It must be noted that, all the techniques and methods developed in this thesis have been assessed on these two real old text documents. Then, a CAT approach for HTR when user e ort is limited is studied and extensively tested. The ultimate goal of applying CAT is achieved by putting together three processes. Given a recognised transcription from an HTR system. The rst process consists in locating (possibly) incorrect words and employs the user e ort available to supervise them (if necessary). As most words are not expected to be supervised due to the limited user e ort available, only a few are selected to be revised. The system presents to the user a small subset of these words according to an estimation of their correctness, or to be more precise, according to their con dence level. Next, the second process starts once these low con dence words have been supervised. This process updates the recogni- tion of the document taking user corrections into consideration, which improves the quality of those words that were not revised by the user. Finally, the last process adapts the system from the partially revised (and possibly not perfect) transcription obtained so far. In this adaptation, the system intelligently selects the correct words of the transcription. As results, the adapted system will bet- ter recognise future transcriptions. Transcription experiments using this CAT approach show that this approach is mostly e ective when user e ort is low. The last contribution of this thesis is a method for balancing the nal tran- scription quality and the supervision e ort applied using our previously de- scribed CAT approach. In other words, this method allows the user to control the amount of errors in the transcriptions obtained from a CAT approach. The motivation of this method is to let users decide on the nal quality of the desired documents, as partially erroneous transcriptions can be su cient to convey the meaning, and the user e ort required to transcribe them might be signi cantly lower when compared to obtaining a totally manual transcription. Consequently, the system estimates the minimum user e ort required to reach the amount of error de ned by the user. Error estimation is performed by computing sepa- rately the error produced by each recognised word, and thus, asking the user to only revise the ones in which most errors occur. Additionally, an interactive prototype is presented, which integrates most of the interactive techniques presented in this thesis. This prototype has been developed to be used by palaeographic expert, who do not have any background in HTR technologies. After a slight ne tuning by a HTR expert, the prototype lets the transcribers to manually annotate the document or employ the CAT ap- proach presented. All automatic operations, such as recognition, are performed in background, detaching the transcriber from the details of the system. The prototype was assessed by an expert transcriber and showed to be adequate and e cient for its purpose. The prototype is freely available under a GNU Public Licence (GPL).Serrano Martínez-Santos, N. (2014). Interactive Transcription of Old Text Documents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37979TESI

Separability versus prototypicality in handwritten word-image retrieval

Hit lists are at the core of retrieval systems. The top ranks are important, especially if user feedback is used to train the system. Analysis of hit lists revealed counter-intuitive instances in the top ranks for good classifiers. In this study, we propose that two functions need to be optimised: (a) in order to reduce a massive set of instances to a likely subset among ten thousand or more classes, separability is required. However, the results need to be intuitive after ranking, reflecting (b) the prototypicality of instances. By optimising these requirements sequentially, the number of distracting images is strongly reduced, followed by nearest-centroid based instance ranking that retains an intuitive (low-edit distance) ranking. We show that in handwritten word-image retrieval, precision improvements of up to 35 percentage points can be achieved, yielding up to 100% top hit precision and 99% top-7 precision in data sets with 84 000 instances, while maintaining high recall performances. The method is conveniently implemented in a massive scale, continuously trainable retrieval engine, Monk. (C) 2013 Elsevier Ltd. All rights reserved

Multiple Contributions to Interactive Transcription and Translation of Old Text Documents

There are huge historical document collections residing in libraries, museums and archives that are currently being digitized for preservation purposes and to make them available worldwide through large, on-line digital libraries. The main objective, however, is not to simply provide access to raw images of digitized documents, but to annotate them with their real informative content and, in particular, with text transcriptions and, if convenient, text translations too. This work aims at contributing to the development of advanced techniques and interfaces for the analysis, transcription and translation of images of old archive documents, following an interactive-predictive approach.Serrano Martínez-Santos, N. (2009). Multiple Contributions to Interactive Transcription and Translation of Old Text Documents. http://hdl.handle.net/10251/11272Archivo delegad

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