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Author |
Oriol Ramos Terrades; Alejandro Hector Toselli; Nicolas Serrano; Veronica Romero; Enrique Vidal; Alfons Juan |
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Title |
Interactive layout analysis and transcription systems for historic handwritten documents |
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Conference Article |
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2010 |
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10th ACM Symposium on Document Engineering |
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219–222 |
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Handwriting recognition; Interactive predictive processing; Partial supervision; Interactive layout analysis |
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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. |
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Manchester, United Kingdom |
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Admin @ si @RTS2010 |
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1857 |
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Author |
Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe |
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Title |
Retrieval Guided Unsupervised Multi-domain Image to Image Translation |
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Conference Article |
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2020 |
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28th ACM International Conference on Multimedia |
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Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. |
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DAG; 600.121 |
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Admin @ si @ GLN2020 |
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3497 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Document noise removal using sparse representations over learned dictionary |
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2013 |
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Symposium on Document engineering |
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161-168 |
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best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental
results on several datasets demonstrate the robustness of our method compared with the state-of-the-art. |
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Barcelona; October 2013 |
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978-1-4503-1789-4 |
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ACM-DocEng |
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DAG; 600.061 |
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Admin @ si @ DTR2013a |
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2330 |
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Author |
Gemma Sanchez; Josep Llados; Enric Marti |
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Title |
Segmentation and analysis of linial texture in plans |
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1997 |
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Actes de la conférence Artificielle et Complexité. |
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Structural Texture, Voronoi, Hierarchical Clustering, String Matching. |
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The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph. |
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Paris, France |
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Paris |
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AERFAI |
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DAG;IAM; |
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IAM @ iam @ SLM1997 |
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1649 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Efficient Exemplar Word Spotting |
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Conference Article |
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2012 |
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23rd British Machine Vision Conference |
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67.1- 67.11 |
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In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. |
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1-901725-46-4 |
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BMVC |
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DAG @ dag @ AGF2012 |
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1984 |
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Marçal Rusiñol; Lluis Gomez; A. Landman; M. Silva Constenla; Dimosthenis Karatzas |
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Title |
Automatic Structured Text Reading for License Plates and Utility Meters |
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Conference Article |
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2019 |
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BMVC Workshop on Visual Artificial Intelligence and Entrepreneurship |
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Reading text in images has attracted interest from computer vision researchers for
many years. Our technology focuses on the extraction of structured text – such as serial
numbers, machine readings, product codes, etc. – so that it is able to center its attention just on the relevant textual elements. It is conceived to work in an end-to-end fashion, bypassing any explicit text segmentation stage. In this paper we present two different industrial use cases where we have applied our automatic structured text reading technology. In the first one, we demonstrate an outstanding performance when reading license plates compared to the current state of the art. In the second one, we present results on our solution for reading utility meters. The technology is commercialized by a recently created spin-off company, and both solutions are at different stages of integration with final clients. |
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Cardiff; UK; September 2019 |
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BMVC-VAIE19 |
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DAG; 600.129 |
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Admin @ si @ RGL2019 |
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3283 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke |
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Graph-based k-means clustering: A comparison of the set versus the generalized median graph |
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2009 |
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13th International Conference on Computer Analysis of Images and Patterns |
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5702 |
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342–350 |
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In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. |
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Münster, Germany |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-03766-5 |
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CAIP |
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DAG |
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DAG @ dag @ FVS2009d |
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1219 |
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Author |
Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño |
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Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos |
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2014 |
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CARE workshop |
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Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection |
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We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels. |
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Boston; USA; September 2014 |
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CARE |
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MV; DAG; 600.060; 600.047; 600.077;SIAI |
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Admin @ si @ NBF2014 |
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2504 |
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Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner |
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Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation |
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2011 |
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In Proceedings of the Sixth International Workshop on Digital Technologies in Cartographic Heritage |
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CartoHerit |
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DAG |
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Admin @ si @ RRL2011b |
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1978 |
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J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier |
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Improving Document Matching Performance by Local Descriptor Filtering |
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2015 |
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6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 |
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1216 - 1220 |
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In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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Nancy; France; August 2015 |
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CBDAR |
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DAG; 600.077; 601.223; 600.084 |
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Admin @ si @ CRO2015a |
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2680 |
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