Keyword spotting for self-training of BLSTM NN based handwriting recognition systems

The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we proposes to use a BLSTM NN handwritten recognition system for keyword spotting in order to select new elements. A set of experiments demonstrate the applicability of selftraining on modern and historic data and the benefits of using keyword spotting over previously published self-training schemes.