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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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Title |
ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT |
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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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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; R. 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 |
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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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no |
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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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ICPR |
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DAG |
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no |
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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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Title |
ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling – RRC-LSVT |
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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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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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no |
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Admin @ si @ SNC2019 |
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3339 |
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Author |
Zheng Huang; Kai Chen; Jianhua He; Xiang Bai; Dimosthenis Karatzas; Shijian Lu; CV Jawahar |
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Title |
ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction |
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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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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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Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados |
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Title |
The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification |
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Conference Article |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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1511-1515 |
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In the last years, there has been a growing interest in the analysis of handwritten music scores. In this sense, our goal has been to foster the interest in the analysis of handwritten music scores by the proposal of two different competitions: Staff removal and Writer Identification. Both competitions have been tested on the CVC-MUSCIMA database: a ground-truth of handwritten music score images. This paper describes the competition details, including the dataset and ground-truth, the evaluation metrics, and a short description of the participants, their methods, and the obtained results. |
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Beijing, China |
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978-0-7695-4520-2 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ FDG2011b |
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1794 |
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Dimosthenis Karatzas; Sergi Robles; Joan Mas; Farshad Nourbakhsh; Partha Pratim Roy |
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ICDAR 2011 Robust Reading Competition – Challege 1: Reading Text in Born-Digital Images (Web and Email) |
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Conference Article |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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1485-1490 |
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This paper presents the results of the first Challenge of ICDAR 2011 Robust Reading Competition. Challenge 1 is focused on the extraction of text from born-digital images, specifically from images found in Web pages and emails. The challenge was organized in terms of three tasks that look at different stages of the process: text localization, text segmentation and word recognition. In this paper we present the results of the challenge for all three tasks, and make an open call for continuous participation outside the context of ICDAR 2011. |
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Beijing, China |
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1520-5363 |
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978-1-4577-1350-7 |
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ICDAR |
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DAG |
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Admin @ si @ KRM2011 |
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1793 |
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Dimosthenis Karatzas; Faisal Shafait; Seiichi Uchida; Masakazu Iwamura; Lluis Gomez; Sergi Robles; Joan Mas; David Fernandez; Jon Almazan; Lluis Pere de las Heras |
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ICDAR 2013 Robust Reading Competition |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1484-1493 |
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This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.056 |
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Admin @ si @ KSU2013 |
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2318 |
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N.Nayef; F.Yin; I.Bizid; H.Choi; Y.Feng; Dimosthenis Karatzas; Z.Luo; Umapada Pal; Christophe Rigaud; J. Chazalon; W.Khlif; Muhammad Muzzamil Luqman; Jean-Christophe Burie; C.L.Liu; Jean-Marc Ogier |
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ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1454-1459 |
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Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge. |
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Kyoto; Japan; November 2017 |
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978-1-5386-3586-5 |
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ICDAR |
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DAG; 600.121 |
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Admin @ si @ NYB2017 |
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3097 |
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ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao |
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ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT) |
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Conference Article |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1444-1447 |
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Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods. |
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978-1-5386-3586-5 |
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ICDAR |
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DAG; 600.121 |
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Admin @ si @ YCY2017 |
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3098 |
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