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

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

Neural text line extraction in historical documents: a two-stage clustering approach

Accessibility of the valuable cultural heritage which is hidden in countless scanned historical documents is the motivation for the presented dissertation. The developed (fully automatic) text line extraction methodology combines state-of-the-art machine learning techniques and modern image processing methods. It demonstrates its quality by outperforming several other approaches on a couple of benchmarking datasets. The method is already being used by a wide audience of researchers from different disciplines and thus contributes its (small) part to the aforementioned goal.Das Erschließen des unermesslichen Wissens, welches in unzähligen gescannten historischen Dokumenten verborgen liegt, bildet die Motivation für die vorgelegte Dissertation. Durch das Verknüpfen moderner Verfahren des maschinellen Lernens und der klassischen Bildverarbeitung wird in dieser Arbeit ein vollautomatisches Verfahren zur Extraktion von Textzeilen aus historischen Dokumenten entwickelt. Die Qualität wird auf verschiedensten Datensätzen im Vergleich zu anderen Ansätzen nachgewiesen. Das Verfahren wird bereits durch eine Vielzahl von Forschern verschiedenster Disziplinen genutzt

Deep Learning Methods for Dialogue Act Recognition using Visual Information

Rozpoznávání dialogových aktů (DA) je důležitým krokem v řízení a porozumění dialogu. Tato úloha spočívá v automatickém přiřazení třídy k výroku/promluvě (nebo jeho části) na základě jeho funkce v dialogu (např. prohlášení, otázka, potvrzení atd.). Takováto klasifikace pak pomáhá modelovat a identifikovat strukturu spontánních dialogů. I když je rozpoznávání DA obvykle realizováno na zvukovém signálu (řeči) pomocí modelů pro automatické rozpoznávání řeči, dialogy existují rovněž ve formě obrázků (např. komiksy). Tato práce se zabývá automatickým rozpoznáváním dialogových aktů z obrazových dokumentů. Dle nás se jedná o první pokus o navržení přístupu rozpoznávání DA využívající obrázky jako vstup. Pro tento úkol je nutné extrahovat text z obrázků. Využíváme proto algoritmy z oblasti počítačového vidění a~zpracování obrazu, jako je prahování obrazu, segmentace textu a optické rozpoznávání znaků (OCR). Hlavním přínosem v této oblasti je návrh a implementace OCR modelu založeného na konvolučních a rekurentních neuronových sítích. Také prozkoumáváme různé strategie pro trénování tohoto modelu, včetně generování syntetických dat a technik rozšiřování dat (tzv. augmentace). Dosahujeme vynikajících výsledků OCR v případě, kdy je malé množství trénovacích dat. Mezi naše přínosy tedy patří to, jak vytvořit efektivní OCR systém s~minimálními náklady na ruční anotaci. Dále se zabýváme vícejazyčností v oblasti rozpoznávání DA. Úspěšně jsme použili a nasadili obecný model, který byl trénován všemi dostupnými jazyky, a také další modely, které byly trénovány pouze na jednom jazyce, a vícejazyčnosti je dosaženo pomocí transformací sémantického prostoru. Také zkoumáme techniku přenosu učení (tzv. transfer learning) pro tuto úlohu tam, kde je k dispozici malý počet anotovaných dat. Používáme příznaky jak na úrovni slov, tak i vět a naše modely hlubokých neuronových sítí (včetně architektury Transformer) dosáhly výborných výsledků v oblasti vícejazyčného rozpoznávání dialogových aktů. Pro rozpoznávání DA z obrazových dokumentů navrhujeme nový multimodální model založený na konvoluční a rekurentní neuronové síti. Tento model kombinuje textové a obrazové vstupy. Textová část zpracovává text z OCR, zatímco vizuální část extrahuje obrazové příznaky, které tvoří další vstup do modelu. Text z OCR obsahuje často překlepy nebo jiné lexikální chyby. Demonstrujeme na experimentech, že tento multimodální model využívající dva vstupy dokáže částečně vyvážit ztrátu informace způsobenou chybovostí OCR systému.ObhájenoDialogue act (DA) recognition is an important step of dialogue management and understanding. This task is to automatically assign a label to an utterance (or its part) based on its function in a dialogue (e.g. statement, question, backchannel, etc.). Such utterance-level classification thus helps to model and identify the structure of spontaneous dialogues. Even though DA recognition is usually realized on audio data using an automatic speech recognition engine, the dialogues exist also in a form of images (e.g. comic books). This thesis deals with automatic dialogue act recognition from image documents. To the best of our knowledge, this is the first attempt to propose DA recognition approaches using the images as an input. For this task, it is necessary to extract the text from the images. Therefore, we employ algorithms from the field of computer vision and image processing such as image thresholding, text segmentation, and optical character recognition (OCR). The main contribution in this field is to design and implement a custom OCR model based on convolutional and recurrent neural networks. We also explore different strategies for training such a~model, including synthetic data generation and data augmentation techniques. We achieve new state-of-the-art OCR results in the constraints when only a few training data are available. Summing up, our contribution is hence also presenting an overview of how to create an efficient OCR system with minimal costs. We further deal with the multilinguality in the DA recognition field. We successfully employ one general model that was trained by data from all available languages, as well as several models that are trained on a single language, and cross-linguality is achieved by using semantic space transformations. Moreover, we explore transfer learning for DA recognition where there is a small number of annotated data available. We use word-level and utterance-level features and our models contain deep neural network architectures, including Transformers. We obtain new state-of-the-art results in multi- and cross-lingual DA regonition field. For DA recognition from image documents, we propose and implement a novel multimodal model based on convolutional and recurrent neural network. This model combines text and image inputs. A text part is fed by text tokens from OCR, while the visual part extracts image features that are considered as an auxiliary input. Extracted text from dialogues is often erroneous and contains typos or other lexical errors. We show that the multimodal model deals with the erroneous text and visual information partially balance this loss of information