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Understanding optical music recognition
J Calvo-Zaragoza, JH Jr, A Pacha - ACM Computing Surveys (CSUR), 2020 - dl.acm.org
For over 50 years, researchers have been trying to teach computers to read music notation,
referred to as Optical Music Recognition (OMR). However, this field is still difficult to access …
referred to as Optical Music Recognition (OMR). However, this field is still difficult to access …
Advancements and challenges in handwritten text recognition: A comprehensive survey
Handwritten Text Recognition (HTR) is essential for digitizing historical documents in
different kinds of archives. In this study, we introduce a hybrid form archive written in French …
different kinds of archives. In this study, we introduce a hybrid form archive written in French …
Trocr: Transformer-based optical character recognition with pre-trained models
Text recognition is a long-standing research problem for document digitalization. Existing
approaches are usually built based on CNN for image understanding and RNN for char …
approaches are usually built based on CNN for image understanding and RNN for char …
Sequence-to-sequence contrastive learning for text recognition
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual
representations, which we apply to text recognition. To account for the sequence-to …
representations, which we apply to text recognition. To account for the sequence-to …
Pay attention to what you read: non-recurrent handwritten text-line recognition
L Kang, P Riba, M Rusiñol, A Fornés, M Villegas - Pattern Recognition, 2022 - Elsevier
The advent of recurrent neural networks for handwriting recognition marked an important
milestone reaching impressive recognition accuracies despite the great variability that we …
milestone reaching impressive recognition accuracies despite the great variability that we …
End-to-end handwritten paragraph text recognition using a vertical attention network
D Coquenet, C Chatelain… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Unconstrained handwritten text recognition remains challenging for computer vision
systems. Paragraph text recognition is traditionally achieved by two models: the first one for …
systems. Paragraph text recognition is traditionally achieved by two models: the first one for …
Evaluating sequence-to-sequence models for handwritten text recognition
J Michael, R Labahn, T Grüning… - … on Document Analysis …, 2019 - ieeexplore.ieee.org
Encoder-decoder models have become an effective approach for sequence learning tasks
like machine translation, image captioning and speech recognition, but have yet to show …
like machine translation, image captioning and speech recognition, but have yet to show …
Accurate, data-efficient, unconstrained text recognition with convolutional neural networks
M Yousef, KF Hussain, US Mohammed - Pattern Recognition, 2020 - Elsevier
Unconstrained text recognition is an important computer vision task, featuring a wide variety
of different sub-tasks, each with its own set of challenges. One of the biggest promises of …
of different sub-tasks, each with its own set of challenges. One of the biggest promises of …
Sequence-to-sequence domain adaptation network for robust text image recognition
Abstract Domain adaptation has shown promising advances for alleviating domain shift
problem. However, recent visual domain adaptation works usually focus on non-sequential …
problem. However, recent visual domain adaptation works usually focus on non-sequential …
[HTML][HTML] Attention-based CNN-RNN Arabic text recognition from natural scene images
According to statistics, there are 422 million speakers of the Arabic language. Islam is the
second-largest religion in the world, and its followers constitute approximately 25% of the …
second-largest religion in the world, and its followers constitute approximately 25% of the …