Synthetic datasets of adversarial images
Published: 9 February 2021| Version 1 | DOI: 10.17632/2g3c836mh3.1
Contributor:
NIDDAL IMAM
Description
We build synthetic datasets of images with embedded adversarial text to improve the robustness of OCR-based spam detectors. The datasets were used in our project (https://github.com/niddal-imam/Post-OCR-Correction).
Files
Steps to reproduce
We choose the most frequent spam words in SMS Spam dataset, toxic words in Jigsaw dataset, and offensive words in OffensEval 2019 dataset using Term Frequency -Inverse Document Frequency (TF-IDF). Then, we used a synthetic data generator (https://github.com/Belval/TextRecognitionDataGenerator) for embedding the perturbed text into images.
Institutions
University of York
Categories
Optical Character Recognition