Gotowe bibliografie tematyczne / Handwritten

Gotowa bibliografia na temat „Handwritten”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 11 września 2023

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Artykuły w czasopismach na temat "Handwritten"

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Mohammed, Israa Bashir, Bashar Saadoon Mahdi i Mustafa Salam Kadhm. "Handwritten signature identification based on MobileNets model and support vector machine classifier". Bulletin of Electrical Engineering and Informatics 12, nr 4 (1.08.2023): 2401–9. http://dx.doi.org/10.11591/beei.v12i4.4965.

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Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.
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Mohammed, Israa Bashir, Bashar Saadoon Mahdi i Mustafa Salam Kadhm. "Handwritten signature identification based on MobileNets model and support vector machine classifier". Bulletin of Electrical Engineering and Informatics 12, nr 4 (1.08.2023): 2401–9. http://dx.doi.org/10.11591/eei.v12i4.4965.

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Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.
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Shinde, Jyoti, Chaitali Rajput i Prof Mrunal Shidore Prof Milind Rane. "Handwritten Digit Recognition". International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (28.02.2018): 608–11. http://dx.doi.org/10.31142/ijtsrd8384.

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ŚNIEŻKO, DARIUSZ. "Handwritten Dedication". Autobiografia 15 (2020): 99–107. http://dx.doi.org/10.18276/au.2020.2.15-08.

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Donaldson, Laurie. "Handwritten circuits". Materials Today 14, nr 9 (wrzesień 2011): 379. http://dx.doi.org/10.1016/s1369-7021(11)70179-1.

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Firdous, Saniya. "Handwritten Character Recognition". International Journal for Research in Applied Science and Engineering Technology 10, nr 5 (31.05.2022): 1409–28. http://dx.doi.org/10.22214/ijraset.2022.42114.

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Singh, Priyanshu, Pranali Pawar i Nikhil Raj. "Handwritten Digit Recognition". International Journal for Research in Applied Science and Engineering Technology 10, nr 5 (31.05.2022): 75–82. http://dx.doi.org/10.22214/ijraset.2022.42062.

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Abstract: Digital recognition is also remarkable an important issue. As handwritten digits are not a same size, thickness, position and direction, in this case by the way, various difficulties should be considered find the handwritten digital recognition problem. I unique and a variety of creative styles for different people moreover have an influence on the model as well the presence of digits. It is a strategy to see again edit written digits. It has a wide variety applications, for example, scheduled bank checks, post offices and tax documents and so on. The purpose of this project is to use the classification algorithm to identify handwritten digits. Background results are probably the most widely used Machine Learning Algorithms such as SVM, KNN and RFC and in-depth reading calculations like CNN multilayer using Keras and Theano and Tensorflow. Using these, 98.70% accuracy was used by CNN (Keras + Theano) compared to 97.91% using SVM, 96.67% using KNN, 96.89% using RFC was obtained. Keywords: SVM, RFC, KNN, CNN
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Saiteja, Jeisetti, Podila Srivally Rao i P. Mani Bharadwaj. "HANDWRITTEN DIGIT RECOGNITION". International Journal of Computer Science and Mobile Computing 11, nr 1 (30.01.2022): 45–54. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.007.

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This project dives into the fundamentals of machine learning victimization associate degree approachable and well-known artificial language, Python. And here we'll be reviewing 2 main components: 1st, we'll be learning regarding the aim of Machine Learning and wherever it applies to the important world. Second, we'll get a general summary of Machine learning topics like supervised learning, model analysis, and Machine Learning algorithms.
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Aref, Walid, Daniel Barbará i Padmavathi Vallabhaneni. "The handwritten trie". ACM SIGMOD Record 24, nr 2 (22.05.1995): 151–62. http://dx.doi.org/10.1145/568271.223811.

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Thomas, Achint Oommen, Amalia Rusu i Venu Govindaraju. "Synthetic handwritten CAPTCHAs". Pattern Recognition 42, nr 12 (grudzień 2009): 3365–73. http://dx.doi.org/10.1016/j.patcog.2008.12.018.

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Rozprawy doktorskie na temat "Handwritten"

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Masrour, Mohsen. "Quiz corrector : Handwritten digits recognition". Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-21615.

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In this project, I want to produce a software method, which can correct quiz papers.Quizzes are multiple-choice questions and the participant should write her/his personal number on the quiz form. Since the Swedish personal numbers are unique, it is sufficient to recognize them to establish the identity of the quiz-participant.  Recognition of this number is important also for correcting the quiz, since the questions and/or answers can be adapted to each participant.  The issue is thus to recognize hand written numerals on quiz-forms.   I used image processing to find suitable features for automatic classification relying on; Logistic regression, or Neural network.
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Allan, Jonathan. "Automated assessment of handwritten scripts". Thesis, Nottingham Trent University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430258.

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Higgins, C. A. "Automatic recognition of handwritten script". Thesis, University of Brighton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372081.

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Lynch, Kathryn Anne. "Handwritten as an industrial body". The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1329426394.

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Mendes, Alexandra Sofia Ferreira. "Structured editing of handwritten mathematics". Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/41239/.

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Teaching effectively requires a clear presentation of the material being taught and interaction with the students. Studies have shown that Tablet PCs provide a good technological support for teaching. The aim of the work presented in this thesis is to design a structure editor of handwritten mathematics that explores the facilities provided by Tablet PCs. The editor is made available in the form of a class library that can be used to extend existing tools. The central feature of the library is the definition of structure for handwritten mathematical expressions which allows syntactic manipulation of expressions. This makes it possible to accurately select, copy and apply algebraic rules, while avoiding the introduction of errors. To facilitate structured manipulation, gestures are used to apply manipulation rules and animations that demonstrate the use of these rules are introduced. Also, some experimental features that can improve the user’s experience and the usability of the library are presented. Furthermore, it is described how to integrate the library into existing tools. In particular, Classroom Presenter, a system developed to create interactive presentations using a Tablet PC, is extended and used to demonstrate how the library’s features can be used in some teaching scenarios. Although there are limitations in the current system, tests performed with teachers and students indicate that it can help to improve the experience of teaching and learning mathematics, particularly calculational mathematics.
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Matsakis, Nicholas E. (Nicholas Elias) 1976. "Recognition of handwritten mathematical expressions". Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/16727.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (leaves 58-59).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
In recent years, the recognition of handwritten mathematical expressions has received an increasing amount of attention in pattern recognition research. The diversity of approaches to the problem and the lack of a commercially viable system, however, indicate that there is still much research to be done in this area. In this thesis, I will describe an on-line approach for converting a handwritten mathematical expression into an equivalent expression in a typesetting command language such as TEX or MathML, as well as a feedback-oriented user interface which can make errors more tolerable to the end user since they can be quickly corrected. The three primary components of this system are a method for classifying isolated handwritten symbols, an algorithm for partitioning an expression into symbols, and an algorithm for converting a two-dimensional arrangements of symbols into a typeset expression. For symbol classification, a Gaussian classifier is used to rank order the interpretations of a set of strokes as a single symbol. To partition an expression, the values generated by the symbol classifier are used to perform a constrained search of possible partitions for the one with the minimum summed cost. Finally, the expression is parsed using a simple geometric grammar.
by Nicholas E. Matsakis.
S.B.and M.Eng.
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Toledo, Testa Juan Ignacio. "Information extraction from heterogeneous handwritten documents". Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667388.

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L’objectiu d’aquesta tesi és l’extracció d’Informació de documents total o parcialment manuscrits amb una certa estructura. Bàsicament treballem amb dos escenaris d’aplicació diferent. El primer escenari són els documents moderns altament estructurats, com formularis. En aquests documents, la informació semàntica està ja definida en camps, amb una posició concreta al document i l’extracció de la informació és equivalent a una transcripció. El segon escenari son els documents semi-estructurats totalment manuscrits on, a més de transcriure, cal associar un valor semàntic, d’entre un conjunt conegut de valors possibles, a les paraules que es transcriuen. En ambdós casos la qualitat de la transcripció té un gran pes en la precisió del sistema, per això proposem models basats en xarxes neuronals per a transcriure text manuscrit. Per a poder afrontar el repte dels documents semi-estructurats hem generat un benchmark, compost de dataset, una sèrie de tasques definides i una mètrica que es va presentar a la comunitat científica com a una competició internacional. També proposem diferents models basats en Xarxes Neuronals Convolucionals i recurrents, capaços de transcriure i assignar diferent etiquetes semàntiques a cada paraula manuscrita, és a dir, capaços d'extreure informació.
El objetivo de esta tesis es la extracción de Información de documentos total o parcialmente manuscritos, con una cierta estructura. Básicamente trabajamos con dos escenarios de aplicación diferentes. El primer escenario son los documentos modernos altamente estructurados, como los formularios. En estos documentos, la información semántica está pre-definida en campos con una posición concreta en el documento i la extracción de información es equivalente a una transcripción. El segundo escenario son los documentos semi-estructurados totalmente manuscritos, donde, además de transcribir, es necesario asociar un valor semántico, de entre un conjunto conocido de valores posibles, a las palabras manuscritas. En ambos casos, la calidad de la transcripción tiene un gran peso en la precisión del sistema. Por ese motivo proponemos modelos basados en redes neuronales para transcribir el texto manuscrito. Para poder afrontar el reto de los documentos semi-estructurados, hemos generado un benchmark, compuesto de dataset, una serie de tareas y una métrica que fue presentado a la comunidad científica a modo de competición internacional. También proponemos diferentes modelos basados en Redes Neuronales Convolucionales y Recurrentes, capaces de transcribir y asignar diferentes etiquetas semánticas a cada palabra manuscrita, es decir, capaces de extraer información.
The goal of this thesis is information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words. In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
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Fan, Rong. "Recognition of dates handwritten on cheques". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39986.pdf.

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Oliveira, Luiz Eduardo Soares. "Automatic recognition of handwritten numerical strings". Mémoire, Montréal : École de technologie supérieure, 2003. http://wwwlib.umi.com/cr/etsmtl/fullcit?pNQ85289.

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Thèse (Ph.D.)--École de technologie supérieure, Montréal, 2003.
"Thesis presented to the École de technologie supérieure in partial fulfillment of the thesis requirement for the degree of philosophiae doctor in engineering". La numérotation de cet ouvrage est erronée. Bibliogr.: f. [149]-163. Également disponible en version électronique.
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Fang, Bin, i 房斌. "Verification of off-line handwritten signatures". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241645.

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Książki na temat "Handwritten"

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Rainbow Cloud Rainbow Cloud Press. Handwritten Family Recipes: Create Your Own Handwritten Recipes Cookbook. Independently Published, 2018.

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Gissing, George. Demos: Handwritten Style. Independently Published, 2021.

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York, Catie. Handwritten for Her. Austin Macauley Publishers Ltd., 2020.

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Conrad, Joseph. Victory: Handwritten Style. Independently Published, 2021.

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Conrad, Joseph. Informer: Handwritten Style. Independently Published, 2021.

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Peper. Handwritten Book Preschoolers. Independently Published, 2022.

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Redburn: Handwritten Style. Independently Published, 2021.

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Beetle: Handwritten Style. Independently Published, 2021.

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York, Catie. Handwritten for Her. Austin Macauley Publishers Ltd., 2020.

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Rudyard, Kipling. Kim: Handwritten Style. Independently Published, 2021.

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Części książek na temat "Handwritten"

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Pérez Castaño, Arnaldo. "Handwritten Digit Recognition". W Practical Artificial Intelligence, 461–78. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3357-3_12.

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Nelli, Fabio. "Recognizing Handwritten Digits". W Python Data Analytics, 473–86. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3913-1_12.

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Nelli, Fabio. "Recognizing Handwritten Digits". W Python Data Analytics, 311–16. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0958-5_11.

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Nelli, Fabio. "Recognizing Handwritten Digits". W Python Data Analytics, 367–84. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9532-8_12.

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Frinken, Volkmar, i Horst Bunke. "Continuous Handwritten Script Recognition". W Handbook of Document Image Processing and Recognition, 391–425. London: Springer London, 2014. http://dx.doi.org/10.1007/978-0-85729-859-1_12.

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Krishnan, Praveen, i C. V. Jawahar. "Matching Handwritten Document Images". W Computer Vision – ECCV 2016, 766–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46448-0_46.

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Watson, Mark. "Recognition of Handwritten Characters". W Common LISP Modules, 71–78. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3186-8_6.

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Garcia-Salicetti, Sonia, Nesma Houmani, Bao Ly-Van, Bernadette Dorizzi, Fernando Alonso-Fernandez, Julian Fierrez, Javier Ortega-Garcia, Claus Vielhauer i Tobias Scheidat. "Online Handwritten Signature Verification". W Guide to Biometric Reference Systems and Performance Evaluation, 125–65. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-292-0_6.

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Chakraborty, Partha, Syeda Surma Jahanapi i Tanupriya Choudhury. "Bangla Handwritten Digit Recognition". W Cyber Intelligence and Information Retrieval, 149–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4284-5_14.

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Livshin, Igor. "Classification of Handwritten Digits". W Artificial Neural Networks with Java, 607–26. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7368-5_14.

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Streszczenia konferencji na temat "Handwritten"

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Albu, Felix, Mihaela Puica i Daniela Hagiescu. "QUALITY EVALUATION APPROACHES OF THE FIRST GRADE CHILDREN'S HANDWRITTING". W eLSE 2014. Editura Universitatii Nationale de Aparare "Carol I", 2014. http://dx.doi.org/10.12753/2066-026x-14-055.

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The evaluation of early school-aged children's handwritten symbols is a challenging problem. The teaching of handwriting is still an essential skill in effective written communication. There is a need for an automatic quality evaluation of handwritten symbols in order to assess the progress of children ability to write nice letters or other symbols. In our case, the letters are positioned in a well defined two-dimensional space, similar to the special notebooks children use in school when learning how to write. For this study, a Wacom system composed of a tablet and a digital pen is used for collecting the data that will be sent to the analysis and evaluation module. While the school-aged child writes a character, the pen transmits the (x,y) coordinates and the time t. Next, a normalization is needed in order to keep the same distance between neighboring pixels. Several approaches are investigated (e.g. normalization with/without interpolation). The coordinates sequence is transformed in a sequence of angles measured relatively to the X axis for each written character. They encode the changing directions during pen movement. An algorithm is used in order to detect if the overall shape of the written symbol is correct. Several parameters that characterise the written character are investigated (e.g. ,,centre of mass", height over width ratio, alignment errors etc.). Their correlation with subjective scores is verified. Several metrics are proposed based on spatial and temporal measurements. Next, the handwritten quality using the legibility, form size and alignment of the letters or digits is investigated. It is shown that a rough discrimination between proficient and non-proficient handwriting can be obtained by considering the size and space parameters. Our simulations have revealed the importance of good handwritten reference samples. The goal is to develop a calligraphic handwritting learning system designed for first grade or pre-school childrens. Further research is needed in order to address other aspects of an intelligent tutor.
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Russell, G., M. P. Perrone, Yi-min Chee i A. Aiman Ziq. "Handwritten document retrieval". W Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition. IEEE, 2002. http://dx.doi.org/10.1109/iwfhr.2002.1030915.

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Pittman, James A. "Recognizing handwritten text". W the SIGCHI conference. New York, New York, USA: ACM Press, 1991. http://dx.doi.org/10.1145/108844.108914.

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Aref, Walid, Daniel Barbará i Padmavathi Vallabhaneni. "The handwritten trie". W the 1995 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/223784.223811.

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Wang, Qingqing, i Yue Lu. "A Sequence Labeling Convolutional Network and Its Application to Handwritten String Recognition". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/411.

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Handwritten string recognition has been struggling with connected patterns fiercely. Segmentation-free and over-segmentation frameworks are commonly applied to deal with this issue. For the past years, RNN combining with CTC has occupied the domain of segmentation-free handwritten string recognition, while CNN is just employed as a single character recognizer in the over-segmentation framework. The main challenges for CNN to directly recognize handwritten strings are the appropriate processing of arbitrary input string length, which implies arbitrary input image size, and reasonable design of the output layer. In this paper, we propose a sequence labeling convolutional network for the recognition of handwritten strings, in particular, the connected patterns. We properly design the structure of the network to predict how many characters present in the input images and what exactly they are at every position. Spatial pyramid pooling (SPP) is utilized with a new implementation to handle arbitrary string length. Moreover, we propose a more flexible pooling strategy called FSPP to adapt the network to the straightforward recognition of long strings better. Experiments conducted on handwritten digital strings from two benchmark datasets and our own cell-phone number dataset demonstrate the superiority of the proposed network.
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Wahi, Amitabh, S. Sundaramurthy i Poovizhi P. "Handwritten Tamil character recognition". W 2013 Fifth International Conference on Advanced Computing (ICoAC). IEEE, 2013. http://dx.doi.org/10.1109/icoac.2013.6921982.

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Govindaraju, Venu, Huaigu Cao i Anurag Bhardwaj. "Handwritten document retrieval strategies". W The Third Workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1568296.1568300.

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Shirke, Archana, Nikunj Gaonkar, Paresh Pandit i Kapil Parab. "Handwritten Gujarati Script Recognition". W 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2021. http://dx.doi.org/10.1109/icaccs51430.2021.9441811.

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"REMOTE HANDWRITTEN SIGNATURE AUTHENTICATION". W 2nd International Conference on E-business and Telecommunication Networks. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001419203350339.

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Al-Taee, May Mowaffaq, Sonia Ben Hassen Neji i Mondher Frikha. "Handwritten Recognition: A survey". W 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2020. http://dx.doi.org/10.1109/ipas50080.2020.9334936.

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Raporty organizacyjne na temat "Handwritten"

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Li, Yi, Yefeng Zheng, David Doermann i Stefan Jaeger. Script-Independent Text Line Segmentation in Freestyle Handwritten Documents. Fort Belvoir, VA: Defense Technical Information Center, grudzień 2006. http://dx.doi.org/10.21236/ada460371.

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Grother, Patrick J. Karhunen Loeve feature extraction for neural handwritten character recognition. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4824.

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Fuller, J. J., A. Farsaie i T. Dumoulin. Handwritten Character Recognition Using Feature Extraction and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, luty 1991. http://dx.doi.org/10.21236/ada238294.

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Mitchell, Renae, i Nicholas Lewis. The woman behind the curious invention of modern software Klára Dán von Neumann’s handwritten letters confirm her legacy. Office of Scientific and Technical Information (OSTI), marzec 2023. http://dx.doi.org/10.2172/1960147.

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Epiphan, Jean, i Steven Handel. Trajectory of forest vegetation under contrasting stressors over a 26-year period, at Morristown National Historical Park: Focused condition assessment report. National Park Service, marzec 2023. http://dx.doi.org/10.36967/2297281.

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The Jockey Hollow section and the New Jersey Brigade Area of Morristown National Historical Park (MORR) are predominantly comprised of upland oak-hickory forests that have regrown over the past 200 years from previous land uses. The forest is being damaged by two major stressors, a large population of white-tailed deer and an abundance of non-native, invasive shrubs and herbaceous species. This study explores changes to the forest over 26 years and suggests management techniques to avoid future degradation. The forest is typical of many upland stands in the region, and studies here would be applicable to many lands controlled by the National Park Service and to many public and private land owners. In 1995, 18 vegetation experimental plots were established in the forest, each 20 x 20 m. Ten plots were in areas that had no non-native, invasive plants. The other eight plots had invasive species. All trees, shrubs and a sampling of herbs were recorded in each of the 18 plots. At that time, no GPS technology was available and handwritten maps were used to record locations. The plots were revisited and resurveyed in 2001; however, only 13 plots were found. This 2021 study is a new survey of the plot conditions. The investigators were able to relocate 17 of the original plots. New GPS locations were recorded for these 17 plots to facilitate future studies. The goal of the study was to test if changes over 26 years in forest conditions differed between the original invaded plots as compared to the uninvaded plots. Also, these data will allow us to measure the progress of invasion into previously uninvaded areas. Together, these results will allow the forest managers to focus attention on the most aggressive plant invaders and to understand the fate of this forest type that is being challenged by deer and non-native plants. Over the last 26 years there has been no hunting for deer here. Also, the plots were not within the few deer exclosures at MORR; deer were able to enter the landscape from surrounding heavily wooded areas. Data were collected in four layers of vegetation – mature trees, saplings, shrubs, and herbaceous groundcovers. The mature trees in the invaded forest plots demonstrated declining trends. The species richness declined by 6%, the average number of trees declined by 30%, white ash and flowering dogwood had the most losses, and basal area did not increase over time because very few new saplings grew into mature tree sizes. The uninvaded plots’ mature trees also revealed a 20% declined in richness, number of trees declined by 18%, the greatest losses occurred for red maple and black birch, but basal area increased slightly due to growth of large persisting trees. Saplings in the invaded forest experienced declines over the 26 years. Species richness declined by 38%, number of native saplings decreased by 44%, and number of invasive saplings increased by 600%. In the uninvaded forest, the conditions and trends were variable. Richness decreased by 21%, no invasive saplings found, number of native saplings increased by 37% (due to increases in American beech). With American beech excluded, the number of native saplings decreased by 60%. In both forest types, the declining number of native saplings was primarily caused by excessive deer damage. For the shrub layer in invaded plots, Japanese barberry stems increased by 122% by 2001 and 276% by 2021. Barberry became the dominant species. Similarly, wineberry stands increased 486% in 2001 and 157% for 2021. It is now the second most common species. However, in the uninvaded plots there was no significant increase in the number of barberry stems and wineberry was not present in 1995 or 2001, and only averaged 1.5 stems per plot in 2021. Neither species has a significant presence now and eradication is possible. A major finding is that the process of invasion of these shrubs over 26 years is very slow. For the herbaceous plants, in the invaded plots there was a sharp decrease in cover by 2021 due to the competitive impact by the abundant invasive shrubs. The invasive Japanese stiltgrass declined 86% in cover and native Carex (sedge) species declined by 78%. In the uninvaded forest plots, stiltgrass was present in very low amounts and did not increase significantly over 26 years. The number of quadrats with any stiltgrass only increased from 3 to 5 over the 26 years. These data show that stiltgrass invades slowly in the uninvaded plots, but in the invaded plots it was greatly replaced by invasive shrubs. Overall, the rate of change of the native herb cover was slow. Together, these data suggest that currently uninvaded forest areas can be protected by monitoring and rigorously eliminating any initial invasions by non-native shrubs and herbs. Once these species reach a significant presence in number of stems or cover in a plot they explode in number. Early intervention when invasive plants first appear is the most practical management tool. Reduction of deer density will also increase the reproductive potential and sustainability of our main forest tree species. Planting of new young canopy tree species, with protection from deer, can retard the ability of invasive plants to begin new populations.
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Savings Bank of New South Wales - Sydney (Head Office) - Secretarial - Legislation and Regulations - Handwritten book of Acts to establish the Savings Bank in N.S.W. & Port Phillip, includes consolidations and amending legislation. (Indexed) - 1832-1848. Reserve Bank of Australia, marzec 2021. http://dx.doi.org/10.47688/rba_archives_2006/21407.

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