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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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Dimosthenis Karatzas; Lluis Gomez; Anguelos Nicolaou; Suman Ghosh; Andrew Bagdanov; Masakazu Iwamura; J. Matas; L. Neumann; V. Ramaseshan; S. Lu ; Faisal Shafait; Seiichi Uchida; Ernest Valveny |
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ICDAR 2015 Competition on Robust Reading |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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1156-1160 |
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ICDAR |
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DAG; 600.077; 600.084 |
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Admin @ si @ KGN2015 |
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2690 |
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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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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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DAG; 600.129; 600.121 |
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Admin @ si @ SNC2019 |
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3339 |
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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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ICDAR 2019 Competition on Scene Text Visual Question Answering |
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2019 |
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3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019 |
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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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CLVL |
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DAG; 600.129; 601.338; 600.135; 600.121 |
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Admin @ si @ BTM2019a |
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3284 |
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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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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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Admin @ si @ BTM2019c |
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3286 |
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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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ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard |
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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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DAG; 600.129; 600.121 |
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Admin @ si @ LZZ2019 |
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3335 |
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Ruben Tito; Minesh Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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ICDAR 2021 Competition on Document Visual Question Answering |
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2021 |
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16th International Conference on Document Analysis and Recognition |
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635-649 |
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In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5, 000 infographics images and 30, 000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented. |
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VIRTUAL; Lausanne; Suissa; September 2021 |
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DAG; 600.121 |
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Admin @ si @ TMJ2021 |
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3624 |
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Wenwen Yu; Mingyu Liu; Mingrui Chen; Ning Lu; Yinlong We; Yuliang Liu; Dimosthenis Karatzas; Xiang Bai |
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ICDAR 2023 Competition on Reading the Seal Title |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14188 |
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522–535 |
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Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2). We constructed a dataset of 10,000 real seal data, covering the most common classes of seals, and labeled all seal title texts with text polygons and text contents. The competition opened on 30th December, 2022 and closed on 20th March, 2023. The competition attracted 53 participants and received 135 submissions from academia and industry, including 28 participants and 72 submissions for Task 1, and 25 participants and 63 submissions for Task 2, which demonstrated significant interest in this challenging task. In this report, we present an overview of the competition, including the organization, challenges, and results. We describe the dataset and tasks, and summarize the submissions and evaluation results. The results show that significant progress has been made in the field of seal title text reading, and we hope that this competition will inspire further research and development in this important area of OCR technology. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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Admin @ si @ YLC2023 |
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3897 |
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George Tom; Minesh Mathew; Sergi Garcia Bordils; Dimosthenis Karatzas; CV Jawahar |
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ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14188 |
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577–586 |
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In this report, we present the final results of the ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition. The RoadText challenge is based on the RoadText-1K dataset and aims to assess and enhance current methods for scene text detection, recognition, and tracking in videos. The RoadText-1K dataset contains 1000 dash cam videos with annotations for text bounding boxes and transcriptions in every frame. The competition features an end-to-end task, requiring systems to accurately detect, track, and recognize text in dash cam videos. The paper presents a comprehensive review of the submitted methods along with a detailed analysis of the results obtained by the methods. The analysis provides valuable insights into the current capabilities and limitations of video text detection, tracking, and recognition systems for dashcam videos. |
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San Jose; CA; USA; August 2023 |
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Admin @ si @ TMG2023 |
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3905 |
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Wenwen Yu; Chengquan Zhang; Haoyu Cao; Wei Hua; Bohan Li; Huang Chen; Mingyu Liu; Mingrui Chen; Jianfeng Kuang; Mengjun Cheng; Yuning Du; Shikun Feng; Xiaoguang Hu; Pengyuan Lyu; Kun Yao; Yuechen Yu; Yuliang Liu; Wanxiang Che; Errui Ding; Cheng-Lin Liu; Jiebo Luo; Shuicheng Yan; Min Zhang; Dimosthenis Karatzas; Xing Sun; Jingdong Wang; Xiang Bai |
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ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14188 |
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536–552 |
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Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot/Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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Admin @ si @ YZC2023 |
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3896 |
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