DDI-100: Dataset for Text Detection and Recognition
This provides a new dataset for researchers in document analysis, addressing a bottleneck in text detection and recognition tasks, though it is incremental as it builds on existing synthetic data methods.
The authors tackled the lack of datasets for text detection and optical character recognition by introducing DDI-100, a synthetic dataset based on 7000 real document pages with over 100,000 augmented images, which demonstrated high-quality performance on real data when validated with several models.
Nowadays document analysis and recognition remain challenging tasks. However, only a few datasets designed for text detection (TD) and optical character recognition (OCR) problems exist. In this paper we present Distorted Document Images dataset (DDI-100) and demonstrate its usefulness in a wide range of document analysis problems. DDI-100 dataset is a synthetic dataset based on 7000 real unique document pages and consists of more than 100000 augmented images. Ground truth comprises text and stamp masks, text and characters bounding boxes with relevant annotations. Validation of DDI-100 dataset was conducted using several TD and OCR models that show high-quality performance on real data.