Early Handwritten Music Recognition with Hidden Markov Models
Jorge Calvo-Zaragoza, Alejandro Héctor Toselli, Enrique Vidal · 2016
This work presents a statistical method to tackle the Handwritten Music Recognition task for Early notation, which comprises more than 200 different symbols. Unlike previous approaches to deal with music notation, our strategy is to perform a holistic recognition without any previous segmentation or staff removal process. The input consists of a page of a music book, which is processed to extract and normalize the staves contained. Then, a feature extraction process is applied to define such sections as a sequence of numerical vectors. The recognition is based on the use of Hidden Markov Models for the optical processing and smoothed N-grams as language model. Experimentation results over a historical archive of Hispanic music reported an error around 40 %, which confirms our proposal as a good starting point taking into account the difficulty of the task.