|
|
Jose Antonio Rodriguez, Florent Perronnin, Gemma Sanchez and Josep Llados. 2008. Unsupervised writer style adaptation for handwritten word spotting. Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award..
|
|
|
|
H. Chouaib, Oriol Ramos Terrades, Salvatore Tabbone, F. Cloppet and N. Vincent. 2008. Feature Selection Combining Genetic Algorithm and Adaboost Classifiers. 19th International Conference on Pattern Recognition.1–4.
|
|
|
|
Salvatore Tabbone, Oriol Ramos Terrades and S. Barrat. 2008. Histogram of radon transform. A useful descriptor for shape retrieval. 19th International Conference on Pattern Recognition.1–4.
|
|
|
|
Partha Pratim Roy, Umapada Pal, Josep Llados and F. Kimura. 2008. Convex Hull based Approach for Multi-oriented Character Recognition form Graphical Documents. 19th International Conference on Pattern Recognition.
|
|
|
|
Raul Gomez, Ali Furkan Biten, Lluis Gomez, Jaume Gibert, Marçal Rusiñol and Dimosthenis Karatzas. 2019. Selective Style Transfer for Text. 15th International Conference on Document Analysis and Recognition.805–812.
Abstract: 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.
Keywords: transfer; text style transfer; data augmentation; scene text detection
|
|
|
|
Ali Furkan Biten and 8 others. 2019. ICDAR 2019 Competition on Scene Text Visual Question Answering. 3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019.
Abstract: 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.
|
|
|
|
Ali Furkan Biten and 8 others. 2019. ICDAR 2019 Competition on Scene Text Visual Question Answering. 15th International Conference on Document Analysis and Recognition.1563–1570.
Abstract: 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.
|
|
|
|
Rui Zhang and 14 others. 2019. ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard. 15th International Conference on Document Analysis and Recognition.1577–1581.
Abstract: 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.
|
|
|
|
Helena Muñoz, Fernando Vilariño and Dimosthenis Karatzas. 2019. Eye-Movements During Information Extraction from Administrative Documents. International Conference on Document Analysis and Recognition Workshops.6–9.
Abstract: 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.
|
|
|
|
Mohammed Al Rawi, Ernest Valveny and Dimosthenis Karatzas. 2019. Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting? 15th International Conference on Document Analysis and Recognition.260–267.
Abstract: 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.
|
|
