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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke |
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Title |
Generalized Median Graph Computation by Means of Graph Embedding in Vector Spaces |
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Journal Article |
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2010 |
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Pattern Recognition |
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PR |
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43 |
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4 |
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1642–1655 |
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Graph matching; Weighted mean of graphs; Median graph; Graph embedding; Vector spaces |
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The median graph has been presented as a useful tool to represent a set of graphs. Nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data. In this paper we propose a new approach for the computation of the median graph based on graph embedding. Graphs are embedded into a vector space and the median is computed in the vector domain. We have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph. Experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. These experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed. |
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DAG @ dag @ FVS2010 |
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1294 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
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Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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Journal Article |
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2009 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
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9 |
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1630–1644 |
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The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG @ dag @ RVT2009 |
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1220 |
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Marçal Rusiñol; Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny; Josep Llados |
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Title |
Perceptual Image Retrieval by Adding Color Information to the Shape Context Descriptor |
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2010 |
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20th International Conference on Pattern Recognition |
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1594–1597 |
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In this paper we present a method for the retrieval of images in terms of perceptual similarity. Local color information is added to the shape context descriptor in order to obtain an object description integrating both shape and color as visual cues. We use a color naming algorithm in order to represent the color information from a perceptual point of view. The proposed method has been tested in two different applications, an object retrieval scenario based on color sketch queries and a color trademark retrieval problem. Experimental results show that the addition of the color information significantly outperforms the sole use of the shape context descriptor. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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DAG @ dag @ RNK2010 |
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1435 |
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Nibal Nayef; Yash Patel; Michal Busta; Pinaki Nath Chowdhury; Dimosthenis Karatzas; Wafa Khlif; Jiri Matas; Umapada Pal; Jean-Christophe Burie; Cheng-lin Liu; Jean-Marc Ogier |
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ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019 |
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Conference Article |
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Year |
2019 |
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15th International Conference on Document Analysis and Recognition |
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1582-1587 |
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With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.121; 600.129 |
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Admin @ si @ NPB2019 |
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3341 |
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Rui Zhang; Yongsheng Zhou; Qianyi Jiang; Qi Song; Nan Li; Kai Zhou; Lei Wang; Dong Wang; Minghui Liao; Mingkun Yang; Xiang Bai; Baoguang Shi; Dimosthenis Karatzas; Shijian Lu; CV Jawahar |
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Title |
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard |
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Conference Article |
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Year |
2019 |
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15th International Conference on Document Analysis and Recognition |
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1577-1581 |
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Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinesecharacters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019-ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A large-scale dataset of 25,000 annotated signboard images, in which all the text lines and characters are annotated with locations and transcriptions, were released. Four tasks, namely character recognition, text line recognition, text line detection and end-to-end recognition were set up. Besides, considering the Chinese text ambiguity issue, we proposed a multi ground truth (multi-GT) evaluation method to make evaluation fairer. The competition started on March 1, 2019 and ended on April 30, 2019. 262 submissions from 46 teams are received. Most of the participants come from universities, research institutes, and tech companies in China. There are also some participants from the United States, Australia, Singapore, and Korea. 21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 600.121 |
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Admin @ si @ LZZ2019 |
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3335 |
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Chee-Kheng Chng; Yuliang Liu; Yipeng Sun; Chun Chet Ng; Canjie Luo; Zihan Ni; ChuanMing Fang; Shuaitao Zhang; Junyu Han; Errui Ding; Jingtuo Liu; Dimosthenis Karatzas; Chee Seng Chan; Lianwen Jin |
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ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT |
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Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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1571-1576 |
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This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 – 82.65%, ii) T2.1 – 74.3%, iii) T2.2 – 85.32%, iv) T3.1 – 53.86%, and v) T3.2 – 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.121; 600.129 |
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no |
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Admin @ si @ CLS2019 |
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3340 |
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Ali Furkan Biten; Ruben Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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Title |
ICDAR 2019 Competition on Scene Text Visual Question Answering |
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Conference Article |
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2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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1563-1570 |
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This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23,038 images annotated with 31,791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that can exploit scene text to achieve holistic image understanding. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 601.338; 600.121 |
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Admin @ si @ BTM2019c |
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3286 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Document segmentation using relative location features |
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2012 |
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21st International Conference on Pattern Recognition |
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1562-1565 |
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In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works. |
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Tsukuba Science City, Japan |
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Admin @ si @ CrR2012 |
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2051 |
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Yipeng Sun; Zihan Ni; Chee-Kheng Chng; Yuliang Liu; Canjie Luo; Chun Chet Ng; Junyu Han; Errui Ding; Jingtuo Liu; Dimosthenis Karatzas; Chee Seng Chan; Lianwen Jin |
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ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling – RRC-LSVT |
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Conference Article |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1557-1562 |
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Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and in-efficient to obtain. To scale up the amount of training data while keeping the labeling procedure cost-effective, this competition introduces a new challenge on Large-scale Street View Text with Partial Labeling (LSVT), providing 50, 000 and 400, 000 images in full and weak annotations, respectively. This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing the gap between research benchmarks and real applications. During the competition period, a total of 41 teams participated in the two proposed tasks with 132 valid submissions, ie, text detection and end-to-end text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of the ICDAR 2019-LSVT challenge. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 600.121 |
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Admin @ si @ SNC2019 |
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3339 |
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Zheng Huang; Kai Chen; Jianhua He; Xiang Bai; Dimosthenis Karatzas; Shijian Lu; CV Jawahar |
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ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction |
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Conference Article |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1516-1520 |
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The ICDAR 2019 Challenge on “Scanned receipts OCR and key information extraction” (SROIE) covers important aspects related to the automated analysis of scanned receipts. The SROIE tasks play a key role in many document analysis systems and hold significant commercial potential. Although a lot of work has been published over the years on administrative document analysis, the community has advanced relatively slowly, as most datasets have been kept private. One of the key contributions of SROIE to the document analysis community is to offer a first, standardized dataset of 1000 whole scanned receipt images and annotations, as well as an evaluation procedure for such tasks. The Challenge is structured around three tasks, namely Scanned Receipt Text Localization (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). The competition opened on 10th February, 2019 and closed on 5th May, 2019. We received 29, 24 and 18 valid submissions received for the three competition tasks, respectively. This report presents the competition datasets, define the tasks and the evaluation protocols, offer detailed submission statistics, as well as an analysis of the submitted performance. While the tasks of text localization and recognition seem to be relatively easy to tackle, it is interesting to observe the variety of ideas and approaches proposed for the information extraction task. According to the submissions' performance we believe there is still margin for improving information extraction performance, although the current dataset would have to grow substantially in following editions. Given the success of the SROIE competition evidenced by the wide interest generated and the healthy number of submissions from academic, research institutes and industry over different countries, we consider that the SROIE competition can evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129 |
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Admin @ si @ HCH2019 |
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3338 |
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