TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text
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Cited by 46 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system. The current systems are crippled by the unavailability of ground truth text annotations for these datasets as well as lack of scene text detection and recognition datasets on real images disallowing the progress in the field of OCR and evaluation of scene text based reasoning in isolation from OCR systems. In this work, we propose TextOCR, an arbitrary-shaped scene text detection and recognition with 900k annotated words collected on real images from TextVQA dataset. We show that current state-of-the-art text-recognition (OCR) models fail to perform well on TextOCR and that training on TextOCR helps achieve state-of-the-art performance on multiple other OCR datasets as well. We use a TextOCR trained OCR model to create PixelM4C model which can do scene text based reasoning on an image in an end-to-end fashion, allowing us to revisit several design choices to achieve new state-of-the-art performance on TextVQA dataset.
Key facts. Cited by 170 papers in the map (data through 7 Oct 2026). Builds on 39 of its 71 references that are in the map. First posted on arXiv 12 May 2021.
Builds on
39 of 71 references.
References · 39
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27 more
- 2016Synthetic Data for Text Localisation in Natural Imagescited by 396
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- 2019ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVTcited by 121
- 2020Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spottingcited by 114
- 2020Captioning Images Taken by People Who Are Blindcited by 108
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- 2019Towards Unconstrained End-to-End Text Spottingcited by 57
- 2019LEAF-QA: Locate, Encode & Attend for Figure Question Answeringcited by 53
- 2020Spatially Aware Multimodal Transformers for TextVQAcited by 44
- 2020Structured Multimodal Attentions for TextVQAcited by 23
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