Benchmarking discriminative approaches for word spotting in handwritten documents

In this article, we propose to benchmark the most popular methods for word spotting in handwritten documents. The benchmark includes a pure HMM approach, as well as hybrid discriminative methods MLP-HMM, CRF-HMM, RNN-HMM and BLSTM-CTC-HMM. This study enables us to observe the increase ratio of performance provided by each discriminative stage compared with the pure generative HMM approach. Moreover, we put forward the different abilities of all these discriminative stages from the simplest MLP to the most complex and current state of the art BLSTM-CTC. We also propose a more specific and original study on BLSTM-CTC, showing that when used as a lexicon-free recognizer, it can reach very interesting word-spotting performance.