Boosting offline handwritten text recognition in historical documents with few labeled lines

TL;DR

This paper improves offline handwritten text recognition in historical documents with limited and noisy labeled data by combining transfer learning, data augmentation, and label error mitigation, achieving significant accuracy gains.

Contribution

It introduces a comprehensive approach integrating transfer learning, data augmentation, and label error correction specifically for low-resource, noisy historical handwritten text recognition.

Findings

Up to 6% reduction in CER on test sets

Effective transfer learning from large to small datasets

Robustness to label errors in training data

Abstract

In this paper, we face the problem of offline handwritten text recognition (HTR) in historical documents when few labeled samples are available and some of them contain errors in the train set. Three main contributions are developed. First we analyze how to perform transfer learning (TL) from a massive database to a smaller historical database, analyzing which layers of the model need a fine-tuning process. Second, we analyze methods to efficiently combine TL and data augmentation (DA). Finally, an algorithm to mitigate the effects of incorrect labelings in the training set is proposed. The methods are analyzed over the ICFHR 2018 competition database, Washington and Parzival. Combining all these techniques, we demonstrate a remarkable reduction of CER (up to 6% in some cases) in the test set with little complexity overhead.