Relevant bibliographies by topics / Automatic Text Recognition (ATR)

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  2. Dissertations / Theses
  3. Books
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  5. Conference papers
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Academic literature on the topic 'Automatic Text Recognition (ATR)'

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Published: 1 February 2025

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Journal articles on the topic "Automatic Text Recognition (ATR)"

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Prebor, Gila. "From Digitization and Images to Text and Content: Transkribus as a Case Study." Proceedings of the Association for Information Science and Technology 60, no. 1 (October 2023): 1102–3. http://dx.doi.org/10.1002/pra2.958.

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ABSTRACTThis poster explores the potential of using technological tools, specifically the Transkribus platform, for the transcription of Hebrew manuscripts. The digitization of historical resources has made them accessible, but the textual content of the scanned images remains inaccessible. Transkribus, an AI‐powered platform, offers tools for text recognition, transcription, and search of historical documents. The poster discusses the process of automatic text recognition (ATR) and the challenges it faces, particularly in handling handwritten texts and Hebrew letters. It provides an overview of the Transkribus platform, its functionalities, and the training process for creating transcription models. The author presents a case study of transcribing a 15th‐century Sephardic semi‐cursive Hebrew manuscript using the Transkribus platform and evaluates the performance of different models. The poster concludes by discussing the implications and possibilities of using Transkribus for automatic transcription of historical Hebrew manuscripts. While the results show promising improvements in accuracy, further challenges and solutions are also discussed. Overall, Transkribus offers significant potential for the study and transcription of Hebrew manuscripts, revolutionizing the field of Jewish studies and historical research.
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R.V, Shalini, Sangamithra G, Shamna A.S, Priyadharshini B, and Raguram M. "Digital Prescription for Hospital Database Management using ASR." International Journal of Computer Communication and Informatics 6, no. 1 (May 25, 2024): 58–69. http://dx.doi.org/10.34256/ijcci2414.

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According to American Medical Association (AMA), handwritten prescriptions are associated with larger risk of pharmaceutical errors when compared to electronic prescriptions. The solution to this problem is to create a digital prescription. This application leverages the usage of automated speech recognition (ASR) technology with digital prescription to make flawless and legible prescriptions. Automatic speech recognition reduces transcribing errors and speeds up prescription processing as well as ensures smooth interface with hospital database management by translating spoken instructions into text in real-time. This innovation not only simplifies clinical workflows but also improves patient safety and database management by providing a reliable and automated method for prescription documentation. This paper presents a digital prescription system for hospital database management using automatic speech recognition (ASR) technology, integrated with MySQL for database management and Java Script for application development. This approach aims to streamline the prescription process, minimize pharmaceutical errors and improve the overall patient care.
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Kit, Chunyu, and Xiaoyue Liu. "Measuring mono-word termhood by rank difference via corpus comparison." Terminology 14, no. 2 (December 12, 2008): 204–29. http://dx.doi.org/10.1075/term.14.2.05kit.

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Terminology as a set of concept carriers crystallizes our special knowledge about a subject. Automatic term recognition (ATR) plays a critical role in the processing and management of various kinds of information, knowledge and documents, e.g., knowledge acquisition via text mining. Measuring termhood properly is one of the core issues involved in ATR. This article presents a novel approach to termhood measurement for mono-word terms via corpus comparison, which quantifies the termhood of a term candidate as its rank difference in a domain and a background corpus. Our ATR experiments to identify legal terms in Hong Kong (HK) legal texts with the British National Corpus (BNC) as background corpus provide evidence to confirm the validity and effectiveness of this approach. Without any prior knowledge and ad hoc heuristics, it achieves a precision of 97.0% on the top 1000 candidates and a precision of 96.1% on the top 10% candidates that are most highly ranked by the termhood measure, illustrating a state-of-the-art performance on mono-word ATR in the field.
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Hu, Jinge. "Automatic Target Recognition of SAR Images Using Collaborative Representation." Computational Intelligence and Neuroscience 2022 (May 24, 2022): 1–7. http://dx.doi.org/10.1155/2022/3100028.

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Synthetic aperture radar (SAR) automatic target recognition (ATR) is one of the key technologies for SAR image interpretation. This paper proposes a SAR target recognition method based on collaborative representation-based classification (CRC). The collaborative coding adopts the global dictionary constructed by training samples of all categories to optimally reconstruct the test samples and determines the target category according to the reconstruction error of each category. Compared with the sparse representation methods, the collaborative representation strategy can improve the representation ability of a small number of training samples for test samples. For SAR target recognition, the resources of training samples are very limited. Therefore, the collaborative representation is more suitable. Based on the MSTAR dataset, the experiments are carried out under a variety of conditions and the proposed method is compared with other classifiers. Experimental results show that the proposed method can achieve superior recognition performance under the standard operating condition (SOC), configuration variances, depression angle variances, and a small number of training samples, which proves its effectiveness.
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Weng, Wenbo. "Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition." Scientific Programming 2023 (April 25, 2023): 1–12. http://dx.doi.org/10.1155/2023/8846287.

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A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to jointly represent them. According to the resulted reconstruction errors of different classes, the target label of test sample can be classified. The moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up the standard operating condition (SOC) and several extended operating conditions (EOCs) including configuration variants, depression angle variances, noise corruption, and partial occlusion to test and validate the proposed method. The results confirm the effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR references.
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Araujo, Gustavo F., Renato Machado, and Mats I. Pettersson. "Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models." Sensors 22, no. 3 (February 8, 2022): 1293. http://dx.doi.org/10.3390/s22031293.

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This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than −5 dB.
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Zhang, Likun, Xiaoyan Li, Yi Tang, Fangbin Song, Tian Xia, and Wei Wang. "Contemporary Advertising Text Art Design and Effect Evaluation by IoT Deep Learning under the Smart City." Security and Communication Networks 2022 (July 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/5161398.

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This work intends to solve the problem that the current artistic typeface generation methods rely too much on manual intervention, lack novelty, and the single font local feature and the global feature extraction method cannot fully describe the font features. Firstly, it proposes a handwritten word recognition model based on generalized search trees (GIST) and the pyramid histogram of oriented gradient (PHOG). The local features and global features of the font are fused. Secondly, a model of automatic artistic typeface generation based on generative adversarial networks (GAN) is constructed, which can use hand-drawn fonts to automatically generate artistic typefaces in the desired style through training as needed. Finally, the generation of the huaniao typeface is used as an example. By constructing the dataset, the effectiveness of the two models is verified. The experimental results show the following: (1) The proposed handwritten character recognition model based on GIST and PHOG has a higher recognition rate of different fonts than the single GIST and PHOG features by more than 5.8%. The total recognition time is reduced by more than 49.4%, and the performance is improved significantly. (2) Compared with other popular algorithms, the constructed GAN-based automatic artistic typeface generation model has the best quality of the generation of huaniao on both the pencil sketch and the calligraphy character image dataset. Models have broad application prospects in contemporary advertising text art design. This study aims to provide important technical support for the automation of contemporary advertising text art design and the improvement of overall efficiency.
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Salamun, Sukri, Khairul Amin, Luluk Elvitaria, and Liza Trisnawati. "Artificial Intelligence Automatic Speech Recognition (ASR) untuk pencarian potongan ayat Al-Qu’ran." Jurnal Komputer Terapan, Vol. 8 No. 1 (2022) (May 31, 2022): 36–45. http://dx.doi.org/10.35143/jkt.v8i1.5299.

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Indonesia merupakan negara dengan jumlah umat muslim terbesar di dunia, yang menjadikan pembacaan ayat-ayat Al-Qur’an sering terdengar di berbagai tempat-tempat umum seperti Mesjid, Mushollah, dan di berbagai kegiatan. Pemanfaatan Automatic Speech Recognition (ASR) sebagai pengenalan kata yang bertujuan untuk mengetahui ayat-ayat Al-Qur’an yang di bacakan untuk menambah pengetahuan mengenai ayat-ayat serta informasi pendukung lainnya sebagai salah satu sarana berdakwah dalam menyampaikan pengetahuan mengenai ayat-ayat Al-Qur’an. Automatic Speech Recognitions (ASR) ini dirancang menggunakan bahasa pemograman Python dan menggunakan framework Django untuk menampilkan informasi mengenai ayat-ayat yang dibacakan dalam bentuk tampilan berbasis web. Penelitian ini bertujuan untuk menciptakan sebuah teknik dan sistem untuk memasukkan perintah suara ke dalam mesin, agar mesin dapat mengerti apa yang manusia ucapkan dan mematuhi apa yang diperintahkannya. Aplikasi ini mengubah data suara menjadi data text menggunakan sistem pengenalan suara yang bekerja secara otomatis dengan pencocokan pola didigitalkan audio kata yang diucapkan terhadap model komputer dari pola bicara untuk menghasilkan keluaran akhir berupa teks yang di simpan didalam database.
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Yu, Xuelian, Hailong Yu, Yi Liu, and Haohao Ren. "Enhanced Prototypical Network with Customized Region-Aware Convolution for Few-Shot SAR ATR." Remote Sensing 16, no. 19 (September 25, 2024): 3563. http://dx.doi.org/10.3390/rs16193563.

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With the prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning-based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning-based methods can achieve outstanding recognition performance on the condition that an abundance of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Therefore, SAR target recognition in the situation of few-shot, where only a scarce few labeled samples are available, is a fundamental problem that needs to be solved. In this paper, a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) is put forward to specially tackle the few-shot SAR ATR tasks. To be specific, a feature-extraction network based on a customized and region-aware convolution is first developed. This network can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, an enhanced prototypical network is proposed. This network can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset, the OpenSARShip dataset, and the SAMPLE+ dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.
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Eck, J. Thomas, and Frank Y. Shih. "An automatic text-free speaker recognition system based on an enhanced Art 2 neural architecture." Information Sciences 76, no. 3-4 (January 1994): 233–53. http://dx.doi.org/10.1016/0020-0255(94)90011-6.

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Dissertations / Theses on the topic "Automatic Text Recognition (ATR)"

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Chiffoleau, Floriane. "Understanding the automatic text recognition process : model training, ground truth and prediction errors." Electronic Thesis or Diss., Le Mans, 2024. http://www.theses.fr/2024LEMA3002.

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Cette thèse travaille à identifier ce qu’un modèle de reconnaissance de texte apprend pendant son entraînement, à travers l’examen du contenu de ses vérités de terrain et de ses erreurs de prédiction. L’intention principale ici est d’améliorer les connaissances sur le fonctionnement d’un réseau de neurones, avec des expériences focalisées sur des documents tapuscrits. Les méthodes utilisées se sont concentrées surtout sur l’exploration approfondie des données d’entraînement, l’observation des erreurs de prédiction des modèles et la corrélation entre les deux. Une première hypothèse, basée sur l’influence du lexique, fut non concluante. Cependant, cela a dirigé les observations vers un nouveau niveau d’étude, s’appuyant sur un niveau infralexical : les n-grammes. La distribution de ceux des données d’entraînement a été analysée et subséquemment, comparée à celle des n-grammes récupérés dans les erreurs de prédiction. Des résultats prometteurs ont conduit à une exploration approfondie, tout en passant d’un modèle de langue unique à un modèle multilingue. Des résultats concluants m’ont permis de déduire que les n-grammes pourraient effectivement être une réponse valide aux performances de reconnaissance
This thesis works on identifying what a text recognition model can learn during its training, through the examination of its ground truth’s content, and its prediction’s errors. The main intent here is to improve the knowledge of how a neural network operates, with experiments focused on typewritten documents. The methods used mostly concentrated on the thorough exploration of the training data, the observation of the model’s prediction’s errors, and the correlation between both. A first hypothesis, based on the influence of the lexicon, was inconclusive. However, it steered the observation towards a new level of study, relying on an infralexical level: the n-grams. Their training data’s distribution was analysed and subsequently compared to that of the n-grams retrieved from the prediction errors. Promising results lead to further exploration, while upgrading from single-language to multilingual model. Conclusive results enabled me to infer that the n-grams might indeed be a valid answer to recognition’s performances
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Gregori, Alessandro <1975&gt. "Automatic Speech Recognition (ASR) and NMT for Interlingual and Intralingual Communication: Speech to Text Technology for Live Subtitling and Accessibility." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9931/1/Gregori_Alessandro_tesi.pdf.

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Considered the increasing demand for institutional translation and the multilingualism of international organizations, the application of Artificial Intelligence (AI) technologies in multilingual communications and for the purposes of accessibility has become an important element in the production of translation and interpreting services (Zetzsche, 2019). In particular, the widespread use of Automatic Speech Recognition (ASR) and Neural Machine Translation (NMT) technology represents a recent development in the attempt of satisfying the increasing demand for interinstitutional, multilingual communications at inter-governmental level (Maslias, 2017). Recently, researchers have been calling for a universalistic view of media and conference accessibility (Greco, 2016). The application of ASR, combined with NMT, may allow for the breaking down of communication barriers at European institutional conferences where multilingualism represents a fundamental pillar (Jopek Bosiacka, 2013). In addition to representing a so-called disruptive technology (Accipio Consulting, 2006), ASR technology may facilitate the communication with non-hearing users (Lewis, 2015). Thanks to ASR, it is possible to guarantee content accessibility for non-hearing audience via subtitles at institutionally-held conferences or speeches. Hence the need for analysing and evaluating ASR output: a quantitative approach is adopted to try to make an evaluation of subtitles, with the objective of assessing its accuracy (Romero-Fresco, 2011). A database of F.A.O.’s and other international institutions’ English-language speeches and conferences on climate change is taken into consideration. The statistical approach is based on WER and NER models (Romero-Fresco, 2016) and on an adapted version. The ASR software solution implemented into the study will be VoxSigma by Vocapia Research and Google Speech Recognition engine. After having defined a taxonomic scheme, Native and Non-Native subtitles are compared to gold standard transcriptions. The intralingual and interlingual output generated by NMT is specifically analysed and evaluated via the NTR model to evaluate accuracy and accessibility.
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Jansson, Annika. "Tal till text för relevant metadatataggning av ljudarkiv hos Sveriges Radio." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169464.

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Tal till text för relevant metadatataggning av ljudarkiv hos Sveriges Radio Sammanfattning Under åren 2009-2013 har Sveriges Radio digitaliserat sitt programarkiv. Sveriges Radios ambition är att mer material från de 175 000 timmar radio som sänds varje år ska arkiveras. Det är en relativt tidsödande process att göra allt material sökbart och det är långt ifrån säkert att kvaliteten på dessa data är lika hög hos alla objekt.         Frågeställningen som har behandlats för detta examensarbete är: Vilka tekniska lösningar finns för att utveckla ett system åt Sveriges Radio för automatisk igenkänning av svenskt tal till text utifrån deras ljudarkiv?         System inom tal till text har analyserats och undersökts för att ge Sveriges Radio en aktuell sammanställning inom området.         Intervjuer med andra liknande organisationer som arbetar inom området har utförts för att se hur långt de har kommit i sin utveckling av det berörda ämnet.         En litteraturstudie har genomförts på de senare forskningsrapporterna inom taligenkänning för att jämföra vilket system som skulle passa Sveriges Radio behov och krav bäst att gå vidare med.         Det Sveriges Radio bör koncentrera sig på först för att kunna bygga en ASR, Automatic Speech Recognition, är att transkribera sitt ljudmaterial. Där finns det tre alternativ, antingen transkribera själva genom att välja ut ett antal program med olika inriktning för att få en så stor bredd som möjligt på innehållet, gärna med olika talare för att sedan även kunna utveckla vidare för igenkänning av talare. Enklaste sättet är att låta olika yrkeskategorier som lägger in inslagen/programmen i systemet göra det. Andra alternativet är att starta ett liknade projekt som BBC har gjort och ta hjälp av allmänheten. Tredje alternativet är att köpa tjänsten för transkribering.         Mitt råd är att fortsätta utvärdera systemet Kaldi, eftersom det har utvecklats mycket på senaste tiden och verkar vara relativt lätt att utvidga. Även den öppna källkod som Lingsoft använder sig av är intressant att studera vidare.
Speech to text for relevant metadata tagging of audio archive at Sveriges Radio Abstract In the years 2009-2013, Sveriges Radio digitized its program archive. Sveriges Radio's ambition is that more material from the 175 000 hours of radio they broadcast every year should be archived. This is a relatively time-consuming process to make all materials to be searchable and it's far from certain that the quality of the data is equally high on all items.         The issue that has been treated for this thesis is: What opportunities exist to develop a system to Sveriges Radio for Swedish speech to text?         Systems for speech to text has been analyzed and examined to give Sveriges Radio a current overview in this subject.         Interviews with other similar organizations working in the field have been performed to see how far they have come in their development of the concerned subject.         A literature study has been conducted on the recent research reports in speech recognition to compare which system would match Sveriges Radio's needs and requirements best to get on with.         What Sveriges Radio should concentrate at first, in order to build an ASR, Automatic Speech Recognition, is to transcribe their audio material. Where there are three alternatives, either transcribe themselves by selecting a number of programs with different orientations to get such a large width as possible on the content, preferably with different speakers and then also be able to develop further recognition of the speaker. The easiest way is to let different professions who make the features/programs in the system do it. Other option is to start a similar project that the BBC has done and take help of the public. The third option is to buy the service for transcription.         My advice is to continue evaluate the Kaldi system, because it has evolved significantly in recent years and seems to be relatively easy to extend. Also the open-source that Lingsoft uses is interesting to study further.
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Gong, XiangQi. "Ellection markup language (EML) based tele-voting system." Thesis, University of the Western Cape, 2009. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_5841_1350999620.

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Elections are one of the most fundamental activities of a democratic society. As is the case in any other aspect of life, developments in technology have resulted changes in the voting procedure from using the traditional paper-based voting to voting by use of electronic means, or e-voting. E-voting involves using different forms of electronic means like
voting machines, voting via the Internet, telephone, SMS and digital interactive television. This thesis concerns voting by telephone, or televoting, it starts by giving a brief overview and evaluation of various models and technologies that are implemented within such systems. The aspects of televoting that have been investigated are technologies that provide a voice interface to the voter and conduct the voting process, namely the Election Markup Language (EML), Automated Speech Recognition (ASR) and Text-to-Speech (TTS).
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Wager, Nicholas. "Automatic Target Recognition (ATR) ATR : background statistics and the detection of targets in clutter /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA293062.

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Thesis (M.S. in Applied Physics) Naval Postgraduate School, December 1994.
Thesis advisor(s): David L. Fried, David Scott Davis. :December 1994." Includes bibliographical references. Also available online.
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Horvath, Matthew Steven. "Performance Prediction of Quantization Based Automatic Target Recognition Algorithms." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452086412.

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Jobbins, Amanda Caryn. "The contribution of semantics to automatic text processing." Thesis, Nottingham Trent University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302405.

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Namane, Abderrahmane. "Degraded printed text and handwritten recognition methods : Application to automatic bank check recognition." Université Louis Pasteur (Strasbourg) (1971-2008), 2007. http://www.theses.fr/2007STR13048.

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La reconnaissance des caractères est une étape importante dans tout système de reconnaissances de document. Cette reconnaissance de caractère est considérée comme un problème d'affectation et de décision de caractères, et a fait l'objet de recherches dans de nombreuses disciplines. Cette thèse porte principalement sur la reconnaissance du caractère imprimé dégradé et manuscrit. De nouvelles solutions ont été apportées au domaine de l'analyse du document image (ADI). On trouve en premier lieu, le développement de deux méthodes de reconnaissance du chiffre manuscrit, notamment, la méthode basée sur l'utilisation de la transformée de Fourier-Mellin (TFM) et la carte auto-organisatrice (CAO), et l'utilisation de la combinaison parallèle basée sur les HMMs comme classificateurs de bases, avec comme extracteur de paramètres une nouvelle technique de projection. En deuxième lieu, on trouve une nouvelle méthode de reconnaissance holistique de mots manuscrits appliquée au montant légal Français. En troisième lieu, deux travaux basés sur les réseaux de neurones ont étés réalisés sur la reconnaissance du caractère imprimé dégradé et appliqués au chèque postal Algérien. Le premier travail est basé sur la combinaison séquentielle et le deuxième a fait l'objet d'une combinaison série basé sur l'introduction d'une distance relative pour la mesure de qualité du caractère dégradé. Lors de l'élaboration de ce travail, des méthodes de prétraitement ont été aussi développées, notamment, la correction de l'inclinaison du chiffre manuscrit, la détection de la zone centrale du mot manuscrit ainsi que sa pente
Character recognition is a significant stage in all document recognition systems. Character recognition is considered as an assignment problem and decision of a given character, and is an active research subject in many disciplines. This thesis is mainly related to the recognition of degraded printed and handwritten characters. New solutions were brought to the field of document image analysis (DIA). The first solution concerns the development of two recognition methods for handwritten numeral character, namely, the method based on the use of Fourier-Mellin transform (FMT) and the self-organization map (SOM), and the parallel combination of HMM-based classifiers using as parameter extraction a new projection technique. In the second solution, one finds a new holistic recognition method of handwritten words applied to French legal amount. The third solution presents two recognition methods based on neural networks for the degraded printed character applied to the Algerian postal check. The first work is based on sequential combination and the second used a serial combination based mainly on the introduction of a relative distance for the quality measurement of the degraded character. During the development of this thesis, methods of preprocessing were also developed, in particular, the handwritten numeral slant correction, the handwritten word central zone detection and its slope
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Bayik, Tuba Makbule. "Automatic Target Recognition In Infrared Imagery." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605388/index.pdf.

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The task of automatically recognizing targets in IR imagery has a history of approximately 25 years of research and development. ATR is an application of pattern recognition and scene analysis in the field of defense industry and it is still one of the challenging problems. This thesis may be viewed as an exploratory study of ATR problem with encouraging recognition algorithms implemented in the area. The examined algorithms are among the solutions to the ATR problem, which are reported to have good performance in the literature. Throughout the study, PCA, subspace LDA, ICA, nearest mean classifier, K nearest neighbors classifier, nearest neighbor classifier, LVQ classifier are implemented and their performances are compared in the aspect of recognition rate. According to the simulation results, the system, which uses the ICA as the feature extractor and LVQ as the classifier, has the best performing results. The good performance of this system is due to the higher order statistics of the data and the success of LVQ in modifying the decision boundaries.
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Bae, Junhyeong. "Adaptive Waveforms for Automatic Target Recognition and Range-Doppler Ambiguity Mitigation in Cognitive Sensor." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/306942.

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This dissertation shows the performance of adaptive waveforms when applied to two radar applications. One application is automatic target recognition (ATR) and the other application is range-Doppler ambiguity mitigation. The adaptive waveforms are implemented via a feedback loop from receiver to transmitter, such that previous radar measurements affect how the adaptive waveforms proceed. For the ATR application, adaptive transmitter can change the waveform's temporal structure to improve target recognition performance. For range-Doppler ambiguity mitigation application, adaptive transmitter can change the pulse repetition frequency (PRF) to mitigate range and Doppler ambiguity. In the ATR application, commercial electromagnetic software is used to create high-fidelity aircraft target signatures. Realistic waveform constraints are applied to show radar performance. The radar equation is incorporated into the waveform design technique and template-based classification is performed. Translation invariant feature is used for inaccurately known range scenario. The performance of adaptive waveforms is evaluated with not only a monostatic radar, but also widely separated MIMO radar. In MIMO radar, multiple transmit waveforms are used, but spectral leakage caused by constant-modulus constraint shows minimal interference effect. In the range-Doppler ambiguity mitigation application, particle-filter-based track-before-detect for a single target is extended to track and detect multiple low signal-to-noise ratio (SNR) targets, simultaneously. To mitigate ambiguity, multiple PRFs are used and improved PRF selection technique is implemented via predicted entropy computation when both blind and clutter zones are considered.
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Books on the topic "Automatic Text Recognition (ATR)"

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Thompson, Karen H. Automatic term recognition in legal text: A feasibility study. Manchester: UMIST, 1996.

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Meisel, William S. The telephony voice user interface: Applications of speech recognition, text-to-speech, and speaker verification over the telephone. Tarzana, CA: TMA Associates, 1998.

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Habernal, Ivan. Text, Speech and Dialogue: 14th International Conference, TSD 2011, Pilsen, Czech Republic, September 1-5, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Griffiths and Blacknell, eds. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/pbra033e.

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Griffiths, Hugh, and David Blacknell. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR). Institution of Engineering & Technology, 2013.

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Griffiths, Hugh, and David Blacknell. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR). Institution of Engineering & Technology, 2013.

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Renals, Steve, and Gregory Grefenstette. Text- and Speech-Triggered Information Access: 8th ELSNET Summer School, Chios Island, Greece, July 15-30, 2000, Revised Lectures. Springer London, Limited, 2006.

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Matousek, Vaclav, and Ivan Habernal. Text, Speech and Dialogue: 16th International Conference, TSD 2013, Pilsen, Czech Republic, September 1-5, 2013, Proceedings. Springer, 2013.

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Habernal, Ivan, and Václav Matousek. Text, Speech, and Dialogue: 16th International Conference, TSD 2013, Pilsen, Czech Republic, September 1-5, 2013, Proceedings. Springer, 2013.

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Habernal, Ivan, and Václav Matousek. Text, Speech and Dialogue: 16th International Conference, TSD 2013, Pilsen, Czech Republic, September 1-5, 2013, Proceedings. Springer, 2013.

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Book chapters on the topic "Automatic Text Recognition (ATR)"

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Formoe, Lars, Dan Bruun Mygind, Espen Løkke, and Hasan Ogul. "Unsupervised Learning for Automatic Speech Recognition in Air Traffic Control Environment." In Text, Speech, and Dialogue, 239–48. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40498-6_21.

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Roberts, Ian, Andres Garcia Silva, Cristian Berrìo Aroca, Jose Manuel Gómez-Pérez, Miroslav Jánoší, Dimitris Galanis, Rémi Calizzano, Andis Lagzdiņš, Milan Straka, and Ulrich Germann. "Language Technology Tools and Services." In European Language Grid, 131–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_7.

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AbstractAt the time of writing, the European Language Grid includes more than 800 LT services of varied types, including machine translation (MT), automatic speech recognition (ASR), text-to-speech synthesis (TTS), and text analysis ranging from simple tokenisers and part-of-speech taggers through to complete named entity recognition and sentiment analysis systems. This chapter gives a high-level summary of the development of the ELG service catalogue over time and digs deeper to discuss the process of service integration by looking at a few example services.
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Popova, Svetlana, Ivan Khodyrev, Irina Ponomareva, and Tatiana Krivosheeva. "Automatic Speech Recognition Texts Clustering." In Text, Speech and Dialogue, 489–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10816-2_59.

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Jelinek, Frederick. "Code Breaking for Automatic Speech Recognition." In Text, Speech and Dialogue, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04208-9_1.

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Anwar, Shamama. "Automatic Text Recognition Using Difference Ratio." In Smart Computing and Informatics, 691–99. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5544-7_68.

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Haderlein, Tino, Stefan Steidl, Elmar Nöth, Frank Rosanowski, and Maria Schuster. "Automatic Recognition and Evaluation of Tracheoesophageal Speech." In Text, Speech and Dialogue, 331–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30120-2_42.

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Urrea, Alfonso Medina, and Jaroslava Hlaváčová. "Automatic Recognition of Czech Derivational Prefixes." In Computational Linguistics and Intelligent Text Processing, 189–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_18.

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Wiggers, Pascal, and Leon J. M. Rothkrantz. "Integration of Speech Recognition and Automatic Lip-Reading." In Text, Speech and Dialogue, 205–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46154-x_28.

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Nouza, Jan, and Radek Safarik. "Parliament Archives Used for Automatic Training of Multi-lingual Automatic Speech Recognition Systems." In Text, Speech, and Dialogue, 174–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64206-2_20.

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Mushtaq, Sumiya, and Neerendra Kumar. "Text-Based Automatic Personality Recognition: Recent Developments." In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security, 537–49. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1142-2_43.

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Conference papers on the topic "Automatic Text Recognition (ATR)"

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Piscaer, Pieter, Lukas Knobel, Lotte Nijskens, Michel van Lier, Judith Dijk, and Nicolas Boehrer. "Improving automatic text recognition through atmospheric turbulence." In Artificial Intelligence for Security and Defence Applications II, edited by Henri Bouma, Yitzhak Yitzhaky, Radhakrishna Prabhu, and Hugo J. Kuijf, 36. SPIE, 2024. http://dx.doi.org/10.1117/12.3031801.

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Mohd Shahadan, Amin Syatir, Huda Adibah Mohd Ramli, Nur Shahida Midi, and Norazlina Saidin. "An Automatic Text Recognition Tool in Signage for the Visually Impaired." In 2024 9th International Conference on Mechatronics Engineering (ICOM), 7–12. IEEE, 2024. http://dx.doi.org/10.1109/icom61675.2024.10652391.

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Sampaio, Matheus Xavier, Regis Pires Magalhães, Ticiana Linhares Coelho da Silva, Lívia Almada Cruz, Davi Romero de Vasconcelos, José Antônio Fernandes de Macêdo, and Marianna Gonçalves Fontenele Ferreira. "Evaluation of Automatic Speech Recognition Systems." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd.2021.17889.

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Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.
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Chen, Chang, Xun Gong, and Yanmin Qian. "Efficient Text-Only Domain Adaptation For CTC-Based ASR." In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2023. http://dx.doi.org/10.1109/asru57964.2023.10389682.

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Cardoso, Walcir, and Danial Mehdipour-Kolour. "Writing with automatic speech recognition: Examining user’s behaviours and text quality (lexical diversity)." In EuroCALL 2023: CALL for all Languages. Editorial Universitat Politécnica de Valéncia: Editorial Universitat Politécnica de Valéncia, 2023. http://dx.doi.org/10.4995/eurocall2023.2023.16997.

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This study explores the potential of Automatic Speech Recognition (ASR) as a writing tool by investigating user behaviours (strategies henceforth) and text quality (lexical diversity) when users engage with the technology. Thirty English second language writers dictated texts into an ASR system (Google Voice Typing) while also using optional additional input devices, such as keyboards and mice. Analysis of video recordings and field observations revealed four strategies employed by users to produce texts: use of ASR exclusively, ASR in tandem with keyboarding, ASR followed by keyboarding, and ASR followed by both keyboarding and ASR. These strategies reflected cognitive differences and text generation challenges. Text quality was operationalized through lexical diversity metrics. Results showed that ASR use in tandem with keyboarding and ASR followed by both keyboarding and ASR yielded greater lexical diversity, whereas the use of ASR exclusively or ASR followed by keyboarding had lower diversity. Findings suggest that the integrated use of ASR and keyboarding activates dual channels, thus dispersing cognitive load and possibly improving text quality (i.e. lexical diversity). This exploratory study demonstrates potential for ASR as a complementary writing tool and lays groundwork for further research on the strategic integration of ASR and keyboarding to improve the quality of written texts.
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Hu, Ke, Tara N. Sainath, Bo Li, Yu Zhang, Yong Cheng, Tao Wang, Yujing Zhang, and Frederick Liu. "Improving Multilingual and Code-Switching ASR Using Large Language Model Generated Text." In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2023. http://dx.doi.org/10.1109/asru57964.2023.10389644.

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Shetty, Shruthi, Hartmut Helmke, Matthias Kleinert, and Oliver Ohneiser. "Early Callsign Highlighting using Automatic Speech Recognition to Reduce Air Traffic Controller Workload." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002493.

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The primary task of an air traffic controller (ATCo) is to issue instructions to pi-lots. However, the first verbal communication contact is often initiated by the pi-lot. Hence, the ATCo needs to search for the aircraft radar label that corresponds to the callsign uttered by the pilot. Therefore, it would be useful to have a control-ler assistance system, which recognizes and highlights the spoken callsign in the ATCo display as early as possible, directly from the speech data. Therefore, we propose to use an automatic speech recognition (ASR) system to first obtain the speech-to-text transcription, followed by extracting the spoken callsign from the transcription. As a high performance in callsign recognition is required, we use surveillance data, which significantly reduces callsign recognition error rates. When using ASR transcriptions for ATCo utterances of Isavia data (HAAWAII project ), we initially obtain a callsign recognition error rate of 6.2%, which im-proves to 2.8% when surveillance data information is used.For the ATC operational speech data obtained from NATS air navigation service provider for London approach area, currently we obtain a callsign recognition rate of 93.8% for both ATCo and pilot utterances on automatic transcriptions which are generated by an ASR system with a word error rate of 5.1%. However, when surveillance data is not used, the callsign recognition rate drops significantly to 82.7%, indicating the importance of using surveillance data while recognizing callsigns. Once the callsign is spoken, we are able to recognize it within a second, which would be of great value to ATCos especially in situations of high traffic constituting high workload.
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Gonçalves, Yanna Torres, João Victor B. Alves, Breno Alef Dourado Sá, Lázaro Natanael da Silva, José A. Fernandes de Macedo, and Ticiana L. Coelho da Silva. "MedTalkAI: Assisted Anamnesis Creation With Automatic Speech Recognition." In Anais Estendidos do Simpósio Brasileiro de Banco de Dados, 83–88. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbbd_estendido.2024.243214.

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Conventional approaches to documenting patient medical histories are often time-consuming and require significant healthcare professional involvement. This paper introduces MedTalkAI, which integrates ASR models, including Whisper and Wav2Vec 2.0, to transcribe audio recordings of patient histories in Brazilian Portuguese efficiently. MedTalkAI validates, corrects, and evaluates transcriptions, facilitating the creation of a unique medical audio-text database. Additionally, MedTalkAI enhances ASR models for medical applications using language models. This approach aims to improve medical history transcription and analysis, contributing to the development of more reliable ASR models and automating the documentation process.
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Negoita, Alexandru, George Suciu, Svetlana Segarceanu, and Dan Trufin. "SPEECH RECOGNITION SYSTEM." In eLSE 2021. ADL Romania, 2021. http://dx.doi.org/10.12753/2066-026x-21-095.

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Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability which enables a program to process human speech into a written format. While it's commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user's voice. Speech recognition applications are becoming more and more useful nowadays. Various interactive speech aware applications are available in the market. But they are usually meant for and executed on the traditional general-purpose computers. With growth in the needs for embedded computing and the demand for emerging embedded platforms, it is required that the speech recognition systems (SRS) are available on them too. Speech recognition systems emerge as efficient alternatives for such devices where typing becomes difficult attributed to their small screen limitations. The paper aims to test a speech recognition system that can be used for a human-machine interaction through speech. The goal is to allow the machine to recognize a set of instructions sent by the user through the voice signal. An automatic speech recognition system will be tested in order to identify words that belong to a limited vocabulary. It will be implemented by engaging a deep neural network (DNN). The construction of the network will be done with the help of the Tensorflow library, which provides support for the development of artificial intelligence algorithms. The system will be tested out on a non-homogeneous group of people, because it is desirable to develop a voice recognition system, independent of the speaker.
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Gonçalves, Yanna Torres, João Victor B. Alves, Breno Alef Dourado Sá, Lázaro Natanael da Silva, José A. Fernandes de Macedo, and Ticiana L. Coelho da Silva. "Speech Recognition Models in Assisting Medical History." In Simpósio Brasileiro de Banco de Dados, 485–97. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbbd.2024.240270.

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This paper addresses challenges highlighted by health professionals, where up to 50\% of a medical consultation's time is spent on history creation. To streamline this process, we propose leveraging Automatic Speech Recognition (ASR) models to convert spoken language into text. In our study, we assess the effectiveness of pre-trained ASR models for medical history transcription in Brazilian Portuguese. By incorporating language models to enhance ASR output, we aim to improve the accuracy and semantic fidelity of transcriptions. Our results demonstrate that integrating a 5-gram model with Wav2Vec2 PT significantly reduces transcription errors, while also maintaining superior performance in capturing textual nuances and similarity.
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Reports on the topic "Automatic Text Recognition (ATR)"

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Oran, D. Requirements for Distributed Control of Automatic Speech Recognition (ASR), Speaker Identification/Speaker Verification (SI/SV), and Text-to-Speech (TTS) Resources. RFC Editor, December 2005. http://dx.doi.org/10.17487/rfc4313.

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Fisher, John, Eric Grimson, and Alan Willsky. A Unified Multiresolution Framework for Automatic Target Recognition (ATR). Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada400047.

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Bhanu, Bir, Yingqiang Lin, and Krzysztof Krawiec. Automatic Design and Synthesis of Automatic Target Recognition (ATR) Systems Using Learning Paradigms. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada424338.

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Xue, Kefu, and Sam Sink. Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) Parametric Study. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada418766.

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Ross, Timothy D., Lori A. Westerkamp, David A. Gadd, and Robert B. Kotz. Feature and Extractor Evaluation Concepts for Automatic Target Recognition (ATR). Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada388215.

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Zucker, Steven W., and Ronald Coifman. Diffusion Maps and Geometric Harmonics for Automatic Target Recognition (ATR). Volume 2. Appendices. Fort Belvoir, VA: Defense Technical Information Center, November 2007. http://dx.doi.org/10.21236/ada476293.

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Moses, Randolph L., Lee C. Potter, and Inder J. Gupta. Feature Extraction Using Attributed Scattering Center Models for Model-Based Automatic Target Recognition (ATR). Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada444563.

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Taylor, James S., and Mary C. Hulgan. Electro-Optic Identification Research Program: Computer Aided Identification (CAI) and Automatic Target Recognition (ATR). Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada624974.

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Griffiths, Rachael M. Handwritten Text Recognition (HTR) for Tibetan Manuscripts in Cursive Script. Verlag der Österreichischen Akademie der Wissenschaften, September 2024. http://dx.doi.org/10.1553/tibschol_erc_htr.

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The use of advanced computational methods for the analysis of digitised texts is becoming increasingly popular in humanities and social science research. One such technology is Handwritten Text Recognition (HTR), which generates transcripts from digitised texts with machine learning approaches, to enable full-text search and analysis. Up to now, HTR models for Tibetan manuscripts in cursive script have not been available. This paper introduces work carried out as part of the The Dawn of Tibetan Buddhist Scholasticism (11th-13th) TibSchol) project at the Austrian Academy of Sciences, which is utilising the Transkribus platform to explore possible solutions to automate the transcription of Tibetan cursive scripts. It presents our methodology and preliminary results along with a discussion of the limitations and potential of our current models.
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Binford, Thomas O., and Tsung-Liang Chen. Context and Quasi-Invariants in Automatic Target Recognition (ATR) with Synthetic Aperture Radar (SAR) Imagery. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada400048.

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