ICDAR 2013 Robust Reading Competition

By Dimosthenis Karatzas, Faisal Shafait, Seiichi Uchida, Masakazu Iwamura, Lluis Gomez i Bigorda, Sergi Robles Mestre, J. M. Romeu, D. F. Mota, Jon Almazán, Lluís-Pere de las Heras. 2013

1,473 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

TAP: Text-Aware Pre-training for Text-VQA and Text-Caption (2020, methodology): “The ST-VQA dataset [8] contains 21,892 images from multiple sources including ICDAR 2013 [27], ICDAR 2015 [26], ImageNet [13], VizWiz [18], IIIT STR [39], Visual Genome [29], and COCO-Text [54].”
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting (2019, methodology): “To confirm the effectiveness of the proposed method on arbitrary-shaped text spotting, we conduct exhaustive experiments and compare with other state-of-the-art methods on four popular benchmarks which consist of a horizontal text set ICDAR2013 (Karatzas et al. 2013), two oriented text sets ICDAR201”
Speeding up Convolutional Neural Networks with Low Rank Expansions (2014, background): “The training dataset consists of 163,222 collected character samples from a number of scene text and synthesized character datasets [1, 2, 5, 17, 21, 34, 42].”
GIT: A Generative Image-to-text Transformer for Vision and Language (2022, methodology): “Following the established setup, we evaluate on six standard benchmarks, including ICDAR 2013 (IC13) (Karatzas et al., 2013), ICDAR 2015 (IC15) (Karatzas et al.”
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition (2014, methodology): “ICDAR 2013 [13] test dataset contains 1015 groundtruth cropped word images from scene text.”
Compression of fully-connected layer in neural network by Kronecker product (2015, methodology): “We also experiment on the word recognition model trained on the synthetic word dataset consisting of 9 million images covering about 90k English words from [4] and tested on ICDAR 2013 [23] dataset.”
TextCaps: a Dataset for Image Captioning with Reading Comprehension (2020, methodology): “To measure the performance of the Rosetta OCR system on TextCaps, we evaluated the precision and recall of OCR tokens against the human-annotated text (ground-truth OCRs) over the validation and test set images, following the ICDAR-13 evaluation protocol for end-to-end text recognition [18].”
Scene Text Visual Question Answering (2019, methodology): “For the strongly contextualised task (1), following the standard practice used for end-to-end word spotting [27, 26, 55], we create a dictionary per image that contains the words that appear in the answers defined for questions on that image, along with a series of distractors.”
Symmetry-based text line detection in natural scenes (2015, methodology): “The experiments on the up-to-date ICDAR benchmarks [32, 13] demonstrate that the proposed algorithm has a broader adaptability (higher detection rate) than conversational methods and outperforms other competing algorithms regarding the final F-measure.”
Spatially Aware Multimodal Transformers for TextVQA (2020, background): “ST-VQA contains 18,921 training and 2,971 test images sourced from several datasets [5, 16, 17, 20, 31].”
Robust Scene Text Recognition with Automatic Rectification (2016, background): “• ICDAR 2013 [20] (IC13) inherits most of its samples from IC03.”
Decoupled Attention Network for Text Recognition (2019, background): “2003) and ICDAR 2013 (Karatzas et al. 2013); and irregular scene text datasets, including SVT-Perspective (Neumann and Matas 2012), CUTE80 (Risnumawan et al.”
Deep Direct Regression for Multi-oriented Scene Text Detection (2017, neutral): “The evaluation protocol is introduced in [12].”