Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Article Segmentation in Digitised Newspapers

Digitisation projects preserve and make available vast quantities of historical text. Among these, newspapers are an invaluable resource for the study of human culture and history. Article segmentation identifies each region in a digitised newspaper page that contains an article. Digital humanities, information retrieval (IR), and natural language processing (NLP) applications over digitised archives improve access to text and allow automatic information extraction. The lack of article segmentation impedes these applications. We contribute a thorough review of the existing approaches to article segmentation. Our analysis reveals divergent interpretations of the task, and inconsistent and often ambiguously defined evaluation metrics, making comparisons between systems challenging. We solve these issues by contributing a detailed task definition that examines the nuances and intricacies of article segmentation that are not immediately apparent. We provide practical guidelines on handling borderline cases and devise a new evaluation framework that allows insightful comparison of existing and future approaches. Our review also reveals that the lack of large datasets hinders meaningful evaluation and limits machine learning approaches. We solve these problems by contributing a distant supervision method for generating large datasets for article segmentation. We manually annotate a portion of our dataset and show that our method produces article segmentations over characters nearly as well as costly human annotators. We reimplement the seminal textual approach to article segmentation (Aiello and Pegoretti, 2006) and show that it does not generalise well when evaluated on a large dataset. We contribute a framework for textual article segmentation that divides the task into two distinct phases: block representation and clustering. We propose several techniques for block representation and contribute a novel highly-compressed semantic representation called similarity embeddings. We evaluate and compare different clustering techniques, and innovatively apply label propagation (Zhu and Ghahramani, 2002) to spread headline labels to similar blocks. Our similarity embeddings and label propagation approach substantially outperforms Aiello and Pegoretti but still falls short of human performance. Exploring visual approaches to article segmentation, we reimplement and analyse the state-of-the-art Bansal et al. (2014) approach. We contribute an innovative 2D Markov model approach that captures reading order dependencies and reduces the structured labelling problem to a Markov chain that we decode with Viterbi (1967). Our approach substantially outperforms Bansal et al., achieves accuracy as good as human annotators, and establishes a new state of the art in article segmentation. Our task definition, evaluation framework, and distant supervision dataset will encourage progress in the task of article segmentation. Our state-of-the-art textual and visual approaches will allow sophisticated IR and NLP applications over digitised newspaper archives, supporting research in the digital humanities

Text-detection and -recognition from natural images

Text detection and recognition from images could have numerous functional applications for document analysis, such as assistance for visually impaired people; recognition of vehicle license plates; evaluation of articles containing tables, street signs, maps, and diagrams; keyword-based image exploration; document retrieval; recognition of parts within industrial automation; content-based extraction; object recognition; address block location; and text-based video indexing. This research exploited the advantages of artificial intelligence (AI) to detect and recognise text from natural images. Machine learning and deep learning were used to accomplish this task.In this research, we conducted an in-depth literature review on the current detection and recognition methods used by researchers to identify the existing challenges, wherein the differences in text resulting from disparity in alignment, style, size, and orientation combined with low image contrast and a complex background make automatic text extraction a considerably challenging and problematic task. Therefore, the state-of-the-art suggested approaches obtain low detection rates (often less than 80%) and recognition rates (often less than 60%). This has led to the development of new approaches. The aim of the study was to develop a robust text detection and recognition method from natural images with high accuracy and recall, which would be used as the target of the experiments. This method could detect all the text in the scene images, despite certain specific features associated with the text pattern. Furthermore, we aimed to find a solution to the two main problems concerning arbitrarily shaped text (horizontal, multi-oriented, and curved text) detection and recognition in a low-resolution scene and with various scales and of different sizes.In this research, we propose a methodology to handle the problem of text detection by using novel combination and selection features to deal with the classification algorithms of the text/non-text regions. The text-region candidates were extracted from the grey-scale images by using the MSER technique. A machine learning-based method was then applied to refine and validate the initial detection. The effectiveness of the features based on the aspect ratio, GLCM, LBP, and HOG descriptors was investigated. The text-region classifiers of MLP, SVM, and RF were trained using selections of these features and their combinations. The publicly available datasets ICDAR 2003 and ICDAR 2011 were used to evaluate the proposed method. This method achieved the state-of-the-art performance by using machine learning methodologies on both databases, and the improvements were significant in terms of Precision, Recall, and F-measure. The F-measure for ICDAR 2003 and ICDAR 2011 was 81% and 84%, respectively. The results showed that the use of a suitable feature combination and selection approach could significantly increase the accuracy of the algorithms.A new dataset has been proposed to fill the gap of character-level annotation and the availability of text in different orientations and of curved text. The proposed dataset was created particularly for deep learning methods which require a massive completed and varying range of training data. The proposed dataset includes 2,100 images annotated at the character and word levels to obtain 38,500 samples of English characters and 12,500 words. Furthermore, an augmentation tool has been proposed to support the proposed dataset. The missing of object detection augmentation tool encroach to proposed tool which has the ability to update the position of bounding boxes after applying transformations on images. This technique helps to increase the number of samples in the dataset and reduce the time of annotations where no annotation is required. The final part of the thesis presents a novel approach for text spotting, which is a new framework for an end-to-end character detection and recognition system designed using an improved SSD convolutional neural network, wherein layers are added to the SSD networks and the aspect ratio of the characters is considered because it is different from that of the other objects. Compared with the other methods considered, the proposed method could detect and recognise characters by training the end-to-end model completely. The performance of the proposed method was better on the proposed dataset; it was 90.34. Furthermore, the F-measure of the method’s accuracy on ICDAR 2015, ICDAR 2013, and SVT was 84.5, 91.9, and 54.8, respectively. On ICDAR13, the method achieved the second-best accuracy. The proposed method could spot text in arbitrarily shaped (horizontal, oriented, and curved) scene text.</div

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Towards robust real-world historical handwriting recognition

In this thesis, we make a bridge from the past to the future by using artificial-intelligence methods for text recognition in a historical Dutch collection of the Natuurkundige Commissie that explored Indonesia (1820-1850). In spite of the successes of systems like 'ChatGPT', reading historical handwriting is still quite challenging for AI. Whereas GPT-like methods work on digital texts, historical manuscripts are only available as an extremely diverse collections of (pixel) images. Despite the great results, current DL methods are very data greedy, time consuming, heavily dependent on the human expert from the humanities for labeling and require machine-learning experts for designing the models. Ideally, the use of deep learning methods should require minimal human effort, have an algorithm observe the evolution of the training process, and avoid inefficient use of the already sparse amount of labeled data. We present several approaches towards dealing with these problems, aiming to improve the robustness of current methods and to improve the autonomy in training. We applied our novel word and line text recognition approaches on nine data sets differing in time period, language, and difficulty: three locally collected historical Latin-based data sets from Naturalis, Leiden; four public Latin-based benchmark data sets for comparability with other approaches; and two Arabic data sets. Using ensemble voting of just five neural networks, a level of accuracy was achieved which required hundreds of neural networks in earlier studies. Moreover, we increased the speed of evaluation of each training epoch without the need of labeled data

Rapid Resource Transfer for Multilingual Natural Language Processing

Until recently the focus of the Natural Language Processing (NLP) community has been on a handful of mostly European languages. However, the rapid changes taking place in the economic and political climate of the world precipitate a similar change to the relative importance given to various languages. The importance of rapidly acquiring NLP resources and computational capabilities in new languages is widely accepted. Statistical NLP models have a distinct advantage over rule-based methods in achieving this goal since they require far less manual labor. However, statistical methods require two fundamental resources for training: (1) online corpora (2) manual annotations. Creating these two resources can be as difficult as porting rule-based methods. This thesis demonstrates the feasibility of acquiring both corpora and annotations by exploiting existing resources for well-studied languages. Basic resources for new languages can be acquired in a rapid and cost-effective manner by utilizing existing resources cross-lingually. Currently, the most viable method of obtaining online corpora is converting existing printed text into electronic form using Optical Character Recognition (OCR). Unfortunately, a language that lacks online corpora most likely lacks OCR as well. We tackle this problem by taking an existing OCR system that was desgined for a specific language and using that OCR system for a language with a similar script. We present a generative OCR model that allows us to post-process output from a non-native OCR system to achieve accuracy close to, or better than, a native one. Furthermore, we show that the performance of a native or trained OCR system can be improved by the same method. Next, we demonstrate cross-utilization of annotations on treebanks. We present an algorithm that projects dependency trees across parallel corpora. We also show that a reasonable quality treebank can be generated by combining projection with a small amount of language-specific post-processing. The projected treebank allows us to train a parser that performs comparably to a parser trained on manually generated data

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