| |
Records |
Links |
|
Author |
Alicia Fornes; Gemma Sanchez |
|
| |
Title |
Analysis and Recognition of Music Scores |
Type |
Book Chapter |
| |
Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
E |
Issue |
|
Pages |
749-774 |
|
| |
Keywords |
|
|
| |
Abstract  |
The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer London |
Place of Publication |
|
Editor |
D. Doermann; K. Tombre |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-0-85729-860-7 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; ADAS; 600.076; 600.077 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FoS2014 |
Serial |
2484 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Oriol Ramos Terrades; Alejandro Hector Toselli; Nicolas Serrano; Veronica Romero; Enrique Vidal; Alfons Juan |
|
| |
Title |
Interactive layout analysis and transcription systems for historic handwritten documents |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
10th ACM Symposium on Document Engineering |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
219–222 |
|
| |
Keywords |
Handwriting recognition; Interactive predictive processing; Partial supervision; Interactive layout analysis |
|
| |
Abstract |
The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents, waiting to be classified and finally transcribed into a textual electronic format (such as ASCII or PDF). Nevertheless, most of the available fully-automatic applications addressing this task are far from being perfect and heavy and inefficient human intervention is often required to check and correct the results of such systems. In contrast, multimodal interactive-predictive approaches may allow the users to participate in the process helping the system to improve the overall performance. With this in mind, two sets of recent advances are introduced in this work: a novel interactive method for text block detection and two multimodal interactive handwritten text transcription systems which use active learning and interactive-predictive technologies in the recognition process. |
|
| |
Address |
Manchester, United Kingdom |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ACM |
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @RTS2010 |
Serial |
1857 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Xavier Otazu; Horst Bunke |
|
| |
Title |
A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores |
Type |
Journal Article |
| |
Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
| |
Volume |
13 |
Issue |
4 |
Pages |
243-259 |
|
| |
Keywords |
|
|
| |
Abstract |
The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; CAT;CIC |
Approved |
no |
|
| |
Call Number |
FLS2010b |
Serial |
1319 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke |
|
| |
Title |
Writer Identification in Old Handwritten Music Scores |
Type |
Book Chapter |
| |
Year |
2012 |
Publication |
Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
27-63 |
|
| |
Keywords |
|
|
| |
Abstract |
The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IGI-Global |
Place of Publication |
|
Editor |
Copnstantin Papaodysseus |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FLS2012 |
Serial |
1828 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
|
| |
Title |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
129 |
Issue |
|
Pages |
108766 |
|
| |
Keywords |
|
|
| |
Abstract |
The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. |
|
| |
Address |
Sept. 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.121; 600.162 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KRR2022 |
Serial |
3556 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya |
|
| |
Title |
Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance |
Type |
Conference Article |
| |
Year |
2024 |
Publication |
Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
718-728 |
|
| |
Keywords |
|
|
| |
Abstract |
The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency. |
|
| |
Address |
Waikoloa; Hawai; USA; January 2024 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
WACV |
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DBB2024 |
Serial |
3986 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol |
|
| |
Title |
The Robust Reading Competition Annotation and Evaluation Platform |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
1st International Workshop on Open Services and Tools for Document Analysis |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation
of data, and to provide online and offline performance evaluation and analysis services |
|
| |
Address |
Kyoto; Japan; November 2017 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICDAR-OST |
|
| |
Notes |
DAG; 600.084; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KGR2017 |
Serial |
3063 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados |
|
| |
Title |
ICDAR2017 Competition on Information Extraction in Historical Handwritten Records |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1389-1394 |
|
| |
Keywords |
|
|
| |
Abstract |
The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results. |
|
| |
Address |
Kyoto; Japan; November 2017 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICDAR |
|
| |
Notes |
DAG; 600.097; 601.225; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FRB2017 |
Serial |
3052 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas |
|
| |
Title |
Single Shot Scene Text Retrieval |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
11218 |
Issue |
|
Pages |
728-744 |
|
| |
Keywords |
Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC |
|
| |
Abstract |
Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed. |
|
| |
Address |
Munich; September 2018 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ECCV |
|
| |
Notes |
DAG; 600.084; 601.338; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GMR2018 |
Serial |
3143 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
|
| |
Title |
TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
|
| |
Abstract |
Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge |
|
| |
Address |
Montreal; Quebec; Canada; August 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
DAG; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BSA2022 |
Serial |
3788 |
|
| Permanent link to this record |
