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- Apostolos AntonacopoulosSuggested from co-authorship
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Works215 from public data
- DocVQA: A Dataset for VQA on Document Images1,800
Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy).
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- InfographicVQA564
The results suggest that VQA on infographics—images that are designed to communicate information quickly and clearly to human brain—is ideal for benchmarking machine understanding of complex document images.
- Scene Text Visual Question Answering545
A new dataset, ST-VQA, is presented that aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the Visual Question Answering process and proposes a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module.
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- ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction452
The ICDAR 2019 Challenge on "Scanned receipts OCR and key information extraction" (SROIE) covers important aspects related to the automated analysis of scanned receipts, and is considered to evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field.
- Exploring Hate Speech Detection in Multimodal Publications322
It is found that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text.
- ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019301
The dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge are presented, which has 4 tasks covering various aspects of multi-lingual scene text.
- ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT272
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) sceneText recognition, and iii) scenetext spotting.
- ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard209
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 and a multi ground truth (multi-GT) evaluation method was proposed to make evaluation fairer.
- ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling - RRC-LSVT207
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 gaps between research benchmarks and real applications.
- Good News, Everyone! Context Driven Entity-Aware Captioning for News Images161
This work proposes a novel captioning method able to leverage contextual information provided by the text of news articles associated with an image, able to selectively draw information from the article guided by visual cues, and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source.
- Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces130
It is shown that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration, and the hidden semantic structures discovered in the text corpus are leverage with a well-known topic modeling technique.
- TextProposals: A text-specific selective search algorithm for word spotting in the wild114
A novel object proposals method that is specifically designed for text, based on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses, that shows competitive performance in end-to-end word spotting and outperforms previously published results.
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- Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning104
By extensive analysis, it is shown that the proposed methods can significantly diminish the authors' models’ object bias on hallucination metrics and experimentally demonstrate that the methods decrease the dependency on the visual features.
- Generation of synthetic documents for performance evaluation of symbol recognition & spotting systems98
A new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context based on the definition of a set of constraints that permits to generate documents with different features that are reflected in variation of localization results.
- ICDAR 2019 Competition on Scene Text Visual Question Answering91
This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA), which 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.
- Improving patch-based scene text script identification with ensembles of conjoined networks90
A patch-based classification method for script identificattion in the wild based on the use of ensembles of conjoined networks and a new public benchmark dataset for the evaluation of multi-lingual scene text end-to-end reading systems.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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