AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions
This addresses the problem of reducing human annotation effort for OCR in domains like personal correspondence or manuscripts, though it is incremental as it builds on existing self-training methods.
The paper tackles text recognition in domains with limited manual annotations by using a self-training strategy with confidence-based data selection and aggressive masking augmentation, achieving up to 55% reduction in character error rate for handwritten data and up to 38% for printed data.
This paper addresses text recognition for domains with limited manual annotations by a simple self-training strategy. Our approach should reduce human annotation effort when target domain data is plentiful, such as when transcribing a collection of single person's correspondence or a large manuscript. We propose to train a seed system on large scale data from related domains mixed with available annotated data from the target domain. The seed system transcribes the unannotated data from the target domain which is then used to train a better system. We study several confidence measures and eventually decide to use the posterior probability of a transcription for data selection. Additionally, we propose to augment the data using an aggressive masking scheme. By self-training, we achieve up to 55 % reduction in character error rate for handwritten data and up to 38 % on printed data. The masking augmentation itself reduces the error rate by about 10 % and its effect is better pronounced in case of difficult handwritten data.