Text Recognition - Real World Data and Where to Find Them

We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as pseudo ground truth (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets.

Automated summary

The method proposed leverages weakly annotated images to enhance text extraction pipelines, by combining imprecise text transcriptions with weak annotations to generate nearly error-free instances of scene text for training, resulting in consistent improvements in accuracy for state-of-the-art recognition models.