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Estimate some global parameters (slant, etc.).
Segment character paths.
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0308
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IEEE DOI
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0309
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A2, A1, Different A3:
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ICPR02(III: 77-80).
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0402
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Fast Feature Selection in an HMM-Based Multiple Classifier System for
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0310
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0308
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A new combination scheme for HMM-based classifiers and its application
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ICPR02(II: 332-337).
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Creation of classifier ensembles for handwritten word recognition using
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FHR02(183-188).
IEEE Top Reference.
0209
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IEEE DOI
0311
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The Segmentation and Identification of Handwriting in Noisy Document
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DAS02(95 ff.).
Springer DOI
0303
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ICPR02(III: 160-163).
IEEE DOI
0211
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Zheng, Y.F.[Ye-Feng],
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Handwriting matching and its application to handwriting synthesis,
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Towards a Ptolemaic Model for OCR,
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0706
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0311
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Word discrimination based on bigram co-occurrences,
ICDAR01(149-153).
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Handwriting; Handwriting synthesis; Writing style; Cursive; Connection
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Handwriting recognition
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Character recognition, Google, Handwriting recognition,
Hidden Markov models, Ink, Training, Writing,
Online handwriting recognition, handwriting, recognition
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Earlier:
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ICDAR15(1116-1120)
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Age; GLBP; HOG; gender; handedness; handwriting recognition
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ICPR21(5527-5534)
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Training, Handwriting recognition, Databases, Supervised learning,
Training data, Psychology, Forestry, Incremental learning,
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Kang, L.[Lei],
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Handwriting text recognition, Transformers, Self-Attention,
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Document analysis and recognition,
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Visualization, Text recognition, Writing, Training,
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Text recognition, Hidden Markov models, Character recognition,
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ICIP21(949-953)
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Training, Handwriting recognition, Uncertainty, Image recognition,
Text recognition, Writing, Handwritten Text Recognition,
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Enhancing Handwritten Text Recognition with N-gram sequence
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ICPR21(10555-10560)
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Training, Text recognition, Computational modeling,
Computer architecture, Network architecture, Decoding, Task analysis
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Chu, W.[Wei],
Variational Connectionist Temporal Classification,
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Sequence labelling problems where the alignment between the inputs and
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Vitug, N.K.U.,
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ICIVC20(204-210)
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Law, Training, Terminology, Testing, Feature extraction, Writing,
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Generative adversarial networks,
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A deep learning approach to handwritten text recognition in the
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IEEE DOI
2004
convolutional neural nets, handwritten character recognition,
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Bhunia, A.K.[Ayan Kumar],
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Bhunia, A.K.[Ankan Kumar],
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Handwriting Recognition in Low-Resource Scripts Using Adversarial
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Mirza, A.[Ali],
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1910
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Toselli, A.H.[Alejandro H.],
Vidal, E.[Enrique],
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Alonso, C.[Carlos],
Marqués, L.[Lourdes],
Modern vs Diplomatic Transcripts for Historical Handwritten Text
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NTIAP19(103-114).
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1909
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Tensmeyer, C.[Chris],
Davis, B.[Brian],
Barrett, W.[William],
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Suwanwiwat, H.[Hemmaphan],
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An Automatic Off-Line Short Answer Assessment System Using Novel
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DICTA16(1-8)
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Earlier: A1, A3, A2:
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ICDAR15(611-615)
IEEE DOI
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Artificial neural networks.
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Kabir, E.[Ehsanollah],
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ICDAR15(1121-1125)
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Vidal, E.[Enrique],
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1511
Handwritten Text Recognition
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ICDAR15(676-680)
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BLSTM-CTC
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Kermorvant, C.[Christopher],
Louradour, J.[Jerome],
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Scribe Attribution for Early Medieval Handwriting by Means of Letter
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Chapter on OCR, Document Analysis and Character Recognition Systems continues in
Cursive Script, Word Level Recognition, Word Spotting, Language Model .