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End to end recognition system for recognizing offline unconstrained vietnamese handwriting

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arXiv 2019年

作者： Le, Anh Duc Nguyen, Hung Tuan T, Masaki Nakagawa Center for Open Data in the Humanities Tokyo Department of Computer and Information Sciences Tokyo University of Agriculture and Technology Tokyo Japan

Inspired by recent successes in neural machine translation and image caption generation, we present an attention based encoder decoder model (AED) to recognize Vietnamese Handwritten Text. The model composes of two parts: a DenseNet for extracting invariant features, and a Long Short-Term Memory network (LSTM) with an attention model incorporated for generating output text (LSTM decoder), which are connected from the CNN part to the attention model. The input of the CNN part is a handwritten text image and the target of the LSTM decoder is the corresponding text of the input image. Our model is trained end-to-end to predict the text from a given input image since all the parts are differential components. In the experiment section, we evaluate our proposed AED model on the VNOnDB-Word and VNOnDB-Line datasets to verify its efficiency. The experiential results show that our model achieves 12.30% of word error rate without using any language model. This result is competitive with the handwriting recognition system provided by Google in the Vietnamese Online Handwritten Text Recognition competition. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

Deep learning approach for receipt recognition

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arXiv 2019年

作者： Le, Anh Duc van Pham, Dung Nguyen, Tuan Anh Center of Open Data in Humanities Research Organization of Information and Systems Tokyo Japan Deep Learning and Application Viet Nam

—Inspired by the recent successes of deep learning on Computer Vision and Natural Language Processing, we present a deep learning approach for recognizing scanned receipts. The recognition system has two main modules: text detection based on Connectionist Text Proposal Network and text recognition based on Attention-based Encoder-Decoder. We also proposed pre-processing to extract receipt area and OCR verification to ignore handwriting. The experiments on the dataset of the Robust Reading Challenge on Scanned Receipts OCR and Information Extraction 2019 demonstrate that the accuracies were improved by integrating the pre-processing and the OCR verification. Our recognition system achieved 71.9% of the F1 score for detection and recognition task. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning

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arXiv 2019年

作者： Clanuwat, Tarin Lamb, Alex Kitamoto, Asanobu Center for Open Data in the Humanities National Institute of Informatics Tokyo Japan MILA Université de Montréal Montreal Canada

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using Machine Learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Nevertheless, several challenges in Kuzushiji recognition have made the performance of existing systems extremely poor. To tackle these challenges, we propose KuroNet, a new end-to-end model which jointly recognizes an entire page of text by using a residual U-Net architecture which predicts the location and identity of all characters given a page of text (without any pre-processing). This allows the model to handle long range context, large vocabularies, and non-standardized character layouts. We demonstrate that our system is able to successfully recognize a large fraction of pre-modern Japanese documents, but also explore areas where our system is limited and suggest directions for future work. Copyright © 2019, The Authors. All rights reserved.

关键词： Character recognition

KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning

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KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition...

International Conference on Document Analysis and Recognition

作者： Tarin Clanuwat Alex Lamb Asanobu Kitamoto Center for Open Data in the Humanities National Institute of Informatics Tokyo Japan MILA Université de Montréal Montreal Canada

关键词： Character recognition Writing Task analysis Printing Training Text recognition Image resolution

An In-depth Analysis of OCR Errors for Unconstrained Vietnamese Handwriting 1

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7th International Conference on Future data and Security Engineering, FDSE 2020

作者： Nguyen, Quoc-Dung Le, Duc-Anh Phan, Nguyet-Minh Zelinka, Ivan Van Lang University 45 Nguyen Khac Nhu Co Giang ward District 1 Ho Chi Minh City Viet Nam Center for Open Data in the Humanities Tokyo Japan University of Information Technology Quarter 6 Linh Trung Ward Thu Duc District Ho Chi Minh City Viet Nam Technical University of Ostrava 17. listopadu 15 Ostrava-Poruba708 33 Czech Republic NTT Hi-Tech Institute Nguyen Tat Thanh University 300A Nguyen Tat Thanh District 4 Ho Chi Minh City Viet Nam

ISBN: (数字)9783030639242

ISBN: (纸本)9783030639235

OCR post-processing is an essential step to improve the accuracy of OCR-generated texts by detecting and correcting OCR errors. In this paper, the OCR texts are resulted from an OCR engine which is based on the attention-based encoder-decoder model for unconstrained Vietnamese handwriting. We identify various kinds of Vietnamese OCR errors and their possible causes. Detailed statistics of Vietnamese OCR errors are provided and analyzed at both character level and syllable level, using typical OCR error characteristics such as error rate, error mapping/edit, frequency and error length. Furthermore, the statistical analyses are done on training and test sets of a benchmark database to infer whether the test set is the appropriate representative of the training set regarding the OCR error characteristics. We also discuss the choice of designing OCR post-processing approaches at character level or at syllable level relying on provided statistics of studied datasets. © 2020, Springer Nature Switzerland AG.

关键词： Decoding

User Interface for Text and Non-Text Classification

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User Interface for Text and Non-Text Classification

International Conference on Document Analysis and Recognition Workshops (ICDARW)

作者： Thanh Thi Xuan Lam Anh Duc Le Masaki Nakagawa Tokyo University of Agriculture and Technology Tokyo Japan Center for Open Data in The Humanities Tokyo Japan Department of Computer and Information Sciences Tokyo University of Agriculture and Technology Tokyo Japan

ISBN: (纸本)9781728150550

In this paper, we propose a user interface for text and non-text classification. The user interface helps a user revise incorrect classification results without rewriting. The user interface automatically switch between writing mode and revising mode without any interruption, which makes a better interaction between the user and the system. We compare the proposed user interface with a simple interface using check-box for switching between the writing and revising modes. The experimental results show that the average time for revising of the touch interface is shorter than checkbox interface. Users also evaluated the touch interface better than the checkbox interface, since they have to do less step when misclassifications occur.

关键词： Writing User interfaces Switches databases Text analysis User experience

Deep Learning for Classical Japanese Literature

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arXiv 2018年

作者： Clanuwat, Tarin Bober-Irizar, Mikel Kitamoto, Asanobu Lamb, Alex Yamamoto, Kazuaki Ha, David Center for Open Data in the Humanities Royal Grammar School Guildford United Kingdom Center for Open Data in the Humanities MILA Université de Montréal National Institute of Japanese Literature Google Brain Tokyo Japan

Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

Knowledge graphs evolution and preservation

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arXiv 2020年

作者： Carriero, Valentina Anita Asprino, Luigi Biswas, Russa d’Amato, Claudia Celino, Irene Domingue, John Dumontier, Michel van Erp, Marieke Paulheim, Heiko Polleres, Axel Presutti, Valentina Rudolph, Sebastian Sack, Harald Abbas, Nacira Alghamdi, Kholoud Alinam, Mortaza Alloatti, Francesca Amaral, Glenda Beno, Martin Bensmann, Felix Cai, Ling Capshaw, Riley Dadoun, Amine de Giorgis, Stefano Delva, Harm Emonet, Vincent Arias, Paola Espinoza Fallatah, Omaima Ferrada, Sebastián Ocaña, Marc Gallofré Georgiou, Michalis Gesese, Genet Asefa Gillis-Webber, Frances Giovannetti, Francesca Buey, María Granados Harrando, Ismail Heibi, Ivan Horta, Vitor Huber, Laurine Igne, Federico Jaradeh, Mohamad Yaser Keshan, Neha Koleva, Aneta Koteich, Bilal Kurniawan, Kabul Liu, Mengya Ma, Chuangtao Maas, Lientje Mansfield, Martin Mariani, Fabio Marzi, Eleonora Mesbah, Sepideh Mistry, Maheshkumar Tirado, Alba Catalina Morales Nguyen, Anna Nguyen, Viet Bach Oelen, Allard Pasqual, Valentina Porena, Margherita Portisch, Jan Pustu-Iren, Kader Mendez, Ariam Rivas Roshankish, Soheil Sakor, Ahmad Salas, Jaime Schleider, Thomas Shi, Meilin Spinaci, Gianmarco Sun, Chang Tietz, Tabea Dhouib, Molka Tounsi Umbrico, Alessandro van den Berg, Wouter Xu, Weiqin Semantic Technology Lab ISTC-CNR IT United States FIZ Karlsruhe Germany University of Bari IT Italy Cefriel MilanIT Italy KMi Open University President of STI International United Kingdom Maastricht University Netherlands DHLab KNAW Humanities Cluster Netherlands University of Mannheim DE Germany Vienna University of Economics and Business AT Austria Semantic Technology Lab ISTC-CNR RomeIT Italy TU Dresden DE Germany FIZ - Karlsruhe Karlsruhe Insitute of Technology AIFB DE Germany INRIA France The University of Southampton United Kingdom University of Genoa Italy University of Torino CELI - Language Technology Italy Free University of Bozen-Bolzano Italy Vienna University of Economics and Business Austria GESIS - Leibniz Institute for the Social Sciences Germany University of California Santa Barbara United States Linköping University Sweden Eurecom France University of Bologna Italy Ghent University IDLab imec Belgium Institute of Data Science Maastricht University Netherlands Universidad Politécnica de Madrid Spain University of Sheffield United Kingdom Institute for Foundational Research on Data University of Chile Chile University of Bergen Norway University of Huddersfield United Kingdom University of Cape Town South Africa everis NTT Data Spain Eurecom - Sorbonne University France Digital Humanities Advanced Research Centre University of Bologna Italy Dublin City University Insight Centre Ireland LORIA Spain University of Oxford United Kingdom L3S Research Center Leibniz University Hannover Germany Rensselaer Polytechnic Institute United States TU Dresden Siemens Munich Germany French naval academy research institute France TU - Wien Austria University of Southampton United Kingdom Eötvös Loránd University Hungary Utrecht University Netherlands The University of Liverpool CSols Ltd United Kingdom Delft University of Technology Netherlands University of Koblenz Institute of Web Science Germany The Open University United Kingdom Ka

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" [24] and described in its report is that of a: Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. For example, Amazon is creating a knowledge graph of all products in the world and Google and Apple have both created knowledge graphs of all locations in the world. This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge. Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by nine teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and

关键词： Knowledge graph

Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France