State-of-the-art methods for handwriting text recognition are based on deep learning approaches and language modeling that require large data sets during training. In practice, there are some applications where the system processes mono-writer documents, and would thus benefit from being trained on examples from that writer. However, this is not common to have numerous examples coming from just one writer. In this paper, we propose an approach to adapt both the optical model and the language model to a particular writer, from a generic system trained on large data sets with a variety of examples. We show the benefits of the optical and language model writer adaptation. Our approach reaches competitive results on the READ 2018 data set, which is dedicated to model adaptation to particular writers.

We introduce a novel handwriting recognition approach based on sub-lexical units known as multigrams of characters, that are variable lengths characters sequences. A Hidden Semi Markov model is used to model the multigrams occurrences within the target language corpus. Decoding the training language corpus with this model provides an optimized multigram lexicon of reduced size with high coverage rate of OOV compared to the traditional word modeling approach. The handwriting recognition system is composed of two components: the optical model and the statistical n-grams of multigrams language model. The two models are combined together during the recognition process using a decoding technique based on Weighted Finite State Transducers (WFST). We experiment the approach on two Latin language datasets (the French RIMES and English IAM datasets) and we show that it outperforms words and character models language models for high Out Of Vocabulary (OOV) words rates, and that it performs similarly to these traditional models for low OOV rates, with the advantage of a reduced complexity.

In this paper, we introduce an iterative method for handwritten text line extraction. The proposed method improves the steerable filter approach by introducing an iterative scheme that iterates lines detection for various configurations of the filters, thus making the method auto adaptable with different types of scripts. We tested the method on different handwritten text datasets, used during earlier competitions organized at ICDAR or ICFHR, and using the same evaluation protocol. The tests carried out on the Open-Hart data set for Arabic scripts, as well as three other Latin and Greek scripts, show that state of the art performance are obtained without tuning any parameters.