TextOCR
Introduced by Singh et al. in TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text
TextOCR is a dataset to benchmark text recognition on arbitrary shaped scene-text. TextOCR requires models to perform text-recognition on arbitrary shaped scene-text present on natural images. TextOCR provides ~1M high quality word annotations on TextVQA images allowing application of end-to-end reasoning on downstream tasks such as visual question answering or image captioning.
Dataset statistics:
- 28,134 natural images from TextVQA
- 903,069 annotated scene-text words
- 32 words per image on average
Papers
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