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Author | Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) | ||||
Title | 16th International Conference, 2021, Proceedings, Part III | Type | Book Whole | ||
Year | 2021 | Publication | Document Analysis and Recognition – ICDAR 2021 | Abbreviated Journal | |
Volume | 12823 | Issue | Pages | ||
Keywords | |||||
Abstract | This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Address | Lausanne, Switzerland, September 5-10, 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Cham | Place of Publication | Editor | Josep Llados; Daniel Lopresti; Seiichi Uchida | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-86333-3 | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3727 | ||
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Author | Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) | ||||
Title | 16th International Conference, 2021, Proceedings, Part IV | Type | Book Whole | ||
Year | 2021 | Publication | Document Analysis and Recognition – ICDAR 2021 | Abbreviated Journal | |
Volume | 12824 | Issue | Pages | ||
Keywords | |||||
Abstract | This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Address | Lausanne, Switzerland, September 5-10, 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Cham | Place of Publication | Editor | Josep Llados; Daniel Lopresti; Seiichi Uchida | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-86336-4 | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3728 | ||
Permanent link to this record | |||||
Author | Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
Title | Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12822 | Issue | Pages | 306-320 | |
Keywords | |||||
Abstract | This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
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 | ICDAR | ||
Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ MRG2021b | Serial | 3571 | ||
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Author | Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal | ||||
Title | DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12823 | Issue | Pages | 555–568 | |
Keywords | |||||
Abstract | Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
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 | |||
Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ BRL2021a | Serial | 3573 | ||
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Author | Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski | ||||
Title | Metric Learning for Session-Based Recommendations | Type | Conference Article | ||
Year | 2021 | Publication | 43rd edition of the annual BCS-IRSG European Conference on Information Retrieval | Abbreviated Journal | |
Volume | 12656 | Issue | Pages | 650-665 | |
Keywords | Session-based recommendations; Deep metric learning; Learning to rank | ||||
Abstract | Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study. | ||||
Address | Virtual; March 2021 | ||||
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 | ECIR | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ TZZ2021 | Serial | 3586 | ||
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Author | Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) | ||||
Title | 16th International Conference, 2021, Proceedings, Part I | Type | Book Whole | ||
Year | 2021 | Publication | Document Analysis and Recognition – ICDAR 2021 | Abbreviated Journal | |
Volume | 12821 | Issue | Pages | ||
Keywords | |||||
Abstract | This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: historical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition. |
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Address | Lausanne, Switzerland, September 5-10, 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Cham | Place of Publication | Editor | Josep Llados; Daniel Lopresti; Seiichi Uchida | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-86548-1 | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3725 | ||
Permanent link to this record | |||||
Author | Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) | ||||
Title | 16th International Conference, 2021, Proceedings, Part II | Type | Book Whole | ||
Year | 2021 | Publication | Document Analysis and Recognition – ICDAR 2021 | Abbreviated Journal | |
Volume | 12822 | Issue | Pages | ||
Keywords | |||||
Abstract | This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Address | Lausanne, Switzerland, September 5-10, 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Cham | Place of Publication | Editor | Josep Llados; Daniel Lopresti; Seiichi Uchida | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-030-86330-2 | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3726 | ||
Permanent link to this record | |||||
Author | Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes | ||||
Title | A Transcription Is All You Need: Learning to Align through Attention | Type | Conference Article | ||
Year | 2021 | Publication | 14th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
Volume | 12916 | Issue | Pages | 141–146 | |
Keywords | |||||
Abstract | Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset. | ||||
Address | Virtual; September 2021 | ||||
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 | GREC | ||
Notes | DAG; 602.230; 600.140; 600.121 | Approved | no | ||
Call Number | Admin @ si @ TSC2021 | Serial | 3619 | ||
Permanent link to this record | |||||
Author | Ruben Tito; Dimosthenis Karatzas; Ernest Valveny | ||||
Title | Document Collection Visual Question Answering | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12822 | Issue | Pages | 778-792 | |
Keywords | Document collection; Visual Question Answering | ||||
Abstract | Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task. | ||||
Address | |||||
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 | ICDAR | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ TKV2021 | Serial | 3622 | ||
Permanent link to this record | |||||
Author | Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus | ||||
Title | Mobile eHealth Platform for Home Monitoring of Bipolar Disorder | Type | Conference Article | ||
Year | 2021 | Publication | 27th ACM International Conference on Multimedia Modeling | Abbreviated Journal | |
Volume | 12573 | Issue | Pages | 330-341 | |
Keywords | |||||
Abstract | People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.
MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians. |
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Address | |||||
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 | MMM | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CEE2021 | Serial | 3659 | ||
Permanent link to this record | |||||
Author | Henry Velesaca; Patricia Suarez; Dario Carpio; Angel Sappa | ||||
Title | Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Symposium on Visual Computing | Abbreviated Journal | |
Volume | 13017 | Issue | Pages | 131–143 | |
Keywords | |||||
Abstract | This paper presents a complete pipeline to perform deep learning-based instance segmentation of different types of grains (e.g., corn, sunflower, soybeans, lentils, chickpeas, mote, and beans). The proposed approach consists of using synthesized image datasets for the training process, which are easily generated according to the category of the instance to be segmented. The synthesized imaging process allows generating a large set of well-annotated grain samples with high variability—as large and high as the user requires. Instance segmentation is performed through a popular deep learning based approach, the Mask R-CNN architecture, but any learning-based instance segmentation approach can be considered. Results obtained by the proposed pipeline show that the strategy of using synthesized image datasets for training instance segmentation helps to avoid the time-consuming image annotation stage, as well as to achieve higher intersection over union and average precision performances. Results obtained with different varieties of grains are shown, as well as comparisons with manually annotated images, showing both the simplicity of the process and the improvements in the performance. | ||||
Address | Virtual; October 2021 | ||||
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 | ISVC | ||
Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ VSC2021 | Serial | 3667 | ||
Permanent link to this record | |||||
Author | Patricia Suarez; Dario Carpio; Angel Sappa | ||||
Title | Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Symposium on Visual Computing | Abbreviated Journal | |
Volume | 13018 | Issue | Pages | 178–190 | |
Keywords | |||||
Abstract | This paper presents a novel attention based architecture to remove non-homogeneous haze. The proposed model is focused on obtaining the most representative characteristics of the image, at each learning cycle, by means of adaptive attention modules coupled with a residual learning convolutional network. The latter is based on the Res2Net model. The proposed architecture is trained with just a few set of images. Its performance is evaluated on a public benchmark—images from the non-homogeneous haze NTIRE 2021 challenge—and compared with state of the art approaches reaching the best result. | ||||
Address | Virtual; October 2021 | ||||
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 | ISVC | ||
Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ SCS2021 | Serial | 3668 | ||
Permanent link to this record | |||||
Author | Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados | ||||
Title | A Self-supervised Inverse Graphics Approach for Sketch Parametrization | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12916 | Issue | Pages | 28-42 | |
Keywords | |||||
Abstract | The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
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 | ICDAR | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ SRR2021 | Serial | 3675 | ||
Permanent link to this record | |||||
Author | Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal | ||||
Title | Graph-Based Deep Generative Modelling for Document Layout Generation | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12917 | Issue | Pages | 525-537 | |
Keywords | |||||
Abstract | One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
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 | |||
Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ BRL2021 | Serial | 3676 | ||
Permanent link to this record | |||||
Author | David Aldavert | ||||
Title | Efficient and Scalable Handwritten Word Spotting on Historical Documents using Bag of Visual Words | Type | Book Whole | ||
Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Word spotting can be defined as the pattern recognition tasked aimed at locating and retrieving a specific keyword within a document image collection without explicitly transcribing the whole corpus. Its use is particularly interesting when applied in scenarios where Optical Character Recognition performs poorly or can not be used at all. This thesis focuses on such a scenario, word spotting on historical handwritten documents that have been written by a single author or by multiple authors with a similar calligraphy.
This problem requires a visual signature that is robust to image artifacts, flexible to accommodate script variations and efficient to retrieve information in a rapid manner. For this, we have developed a set of word spotting methods that on their foundation use the well known Bag-of-Visual-Words (BoVW) representation. This representation has gained popularity among the document image analysis community to characterize handwritten words in an unsupervised manner. However, most approaches on this field rely on a basic BoVW configuration and disregard complex encoding and spatial representations. We determine which BoVW configurations provide the best performance boost to a spotting system. Then, we extend the segmentation-based word spotting, where word candidates are given a priori, to segmentation-free spotting. The proposed approach seeds the document images with overlapping word location candidates and characterizes them with a BoVW signature. Retrieval is achieved comparing the query and candidate signatures and returning the locations that provide a higher consensus. This is a simple but powerful approach that requires a more compact signature than in a segmentation-based scenario. We first project the BoVW signature into a reduced semantic topics space and then compress it further using Product Quantizers. The resulting signature only requires a few dozen bytes, allowing us to index thousands of pages on a common desktop computer. The final system still yields a performance comparable to the state-of-the-art despite all the information loss during the compression phases. Afterwards, we also study how to combine different modalities of information in order to create a query-by-X spotting system where, words are indexed using an information modality and queries are retrieved using another. We consider three different information modalities: visual, textual and audio. Our proposal is to create a latent feature space where features which are semantically related are projected onto the same topics. Creating thus a new feature space where information from different modalities can be compared. Later, we consider the codebook generation and descriptor encoding problem. The codebooks used to encode the BoVW signatures are usually created using an unsupervised clustering algorithm and, they require to test multiple parameters to determine which configuration is best for a certain document collection. We propose a semantic clustering algorithm which allows to estimate the best parameter from data. Since gather annotated data is costly, we use synthetically generated word images. The resulting codebook is database agnostic, i. e. a codebook that yields a good performance on document collections that use the same script. We also propose the use of an additional codebook to approximate descriptors and reduce the descriptor encoding complexity to sub-linear. Finally, we focus on the problem of signatures dimensionality. We propose a new symbol probability signature where each bin represents the probability that a certain symbol is present a certain location of the word image. This signature is extremely compact and combined with compression techniques can represent word images with just a few bytes per signature. |
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Address | April 2021 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Marçal Rusiñol;Josep Llados | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title ![]() |
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-122714-5-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ Ald2021 | Serial | 3601 | ||
Permanent link to this record |