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Found 18 papers, 6 papers with code
Align, Minimize and Diversify: A Source-Free Unsupervised Domain Adaptation Method for Handwritten Text Recognition
This paper serves to introduce the Align, Minimize and Diversify (AMD) method, a Source-Free Unsupervised Domain Adaptation approach for Handwritten Text Recognition (HTR).
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition
Handwritten Text Recognition (HTR) is a relevant problem in computer vision, and implies unique challenges owing to its inherent variability and the rich contextualization required for its interpretation.
SDFR: Synthetic Data for Face Recognition Competition
The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data.
Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription
State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date, primarily been carried out using monophonic transcription techniques to handle complex score layouts, such as polyphony, often by resorting to simplifications or specific adaptations.
Proceedings of the 5th International Workshop on Reading Music Systems
The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists.
Image Transformation Sequence Retrieval with General Reinforcement Learning
In this work, the novel Image Transformation Sequence Retrieval (ITSR) task is presented, in which a model must retrieve the sequence of transformations between two given images that act as source and target, respectively.
Model-based Reinforcement Learning
reinforcement-learning
+1
End-to-End Page-Level Assessment of Handwritten Text Recognition
The evaluation of Handwritten Text Recognition (HTR) systems has traditionally used metrics based on the edit distance between HTR and ground truth (GT) transcripts, at both the character and word levels.
Proceedings of the 1st International Workshop on Reading Music Systems
The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists.
Proceedings of the 3rd International Workshop on Reading Music Systems
The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists.
Proceedings of the 2nd International Workshop on Reading Music Systems
The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists.
Proceedings of the 4th International Workshop on Reading Music Systems
The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists.
Late multimodal fusion for image and audio music transcription
When addressing this challenge in computational terms, the MIR community follows two lines of research: music documents, which is the case of Optical Music Recognition (OMR), or audio recordings, which is the case of Automatic Music Transcription (AMT).
Region-based Layout Analysis of Music Score Images
The Layout Analysis (LA) stage is of vital importance to the correct performance of an Optical Music Recognition (OMR) system.
Unsupervised Neural Domain Adaptation for Document Image Binarization
These techniques take advantage of the knowledge learned in one domain, for which labeled data are available, to apply it to other domains for which there are no labeled data.
Incremental Unsupervised Domain-Adversarial Training of Neural Networks
In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples.
A holistic approach to polyphonic music transcription with neural networks
We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion.
Understanding Optical Music Recognition
For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR).
A selectional auto-encoder approach for document image binarization
Binarization plays a key role in the automatic information retrieval from document images.
