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Author |
H. Chouaib; Oriol Ramos Terrades; Salvatore Tabbone; F. Cloppet; N. Vincent |
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Title |
Feature Selection Combining Genetic Algorithm and Adaboost Classifiers |
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2008 |
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19th International Conference on Pattern Recognition |
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1-4 |
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Tampa, Florida |
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Admin @ si @ CRT2008 |
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1872 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades; S. Barrat |
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Title |
Histogram of radon transform. A useful descriptor for shape retrieval |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition |
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1-4 |
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Tampa, Florida |
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DAG |
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Admin @ si @ TRB2008 |
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1876 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados; F. Kimura |
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Title |
Convex Hull based Approach for Multi-oriented Character Recognition form Graphical Documents |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition |
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Tampa (Florida) |
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DAG @ dag @ RPL2008d |
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1073 |
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Author |
Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Selective Style Transfer for Text |
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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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805-812 |
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transfer; text style transfer; data augmentation; scene text detection |
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This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.129; 600.135; 601.338; 601.310; 600.121 |
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GBG2019 |
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3265 |
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Author |
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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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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Author |
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 |
Type |
Conference Article |
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Year |
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 |
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 |
Publication |
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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Author |
Helena Muñoz; Fernando Vilariño; Dimosthenis Karatzas |
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Title |
Eye-Movements During Information Extraction from Administrative Documents |
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Conference Article |
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2019 |
Publication |
International Conference on Document Analysis and Recognition Workshops |
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6-9 |
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A key aspect of digital mailroom processes is the extraction of relevant information from administrative documents. More often than not, the extraction process cannot be fully automated, and there is instead an important amount of manual intervention. In this work we study the human process of information extraction from invoice document images. We explore whether the gaze of human annotators during an manual information extraction process could be exploited towards reducing the manual effort and automating the process. To this end, we perform an eye-tracking experiment replicating real-life interfaces for information extraction. Through this pilot study we demonstrate that relevant areas in the document can be identified reliably through automatic fixation classification, and the obtained models generalize well to new subjects. Our findings indicate that it is in principle possible to integrate the human in the document image analysis loop, making use of the scanpath to automate the extraction process or verify extracted information. |
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Sydney; Australia; September 2019 |
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ICDARW |
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DAG; 600.140; 600.121; 600.129;SIAI |
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Admin @ si @ MVK2019 |
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3336 |
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Author |
Mohammed Al Rawi; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? |
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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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260-267 |
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Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language. |
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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 @ RVK2019 |
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3337 |
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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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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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