[EN] Handwritten Music Recognition is the branch of Optical Symbol Recognition dedicated to the study of the capability of computers to read written musical notation. This technology aims to understand musical notation to transcribe the handwritten works into a computer-adapted format, to make this music available to the public. This task has been of great interest lately, as the technologies improve and can get better and better results on this problem. Recent machine learning approaches based on Recurrent and Deep Neural Networks have already shown how these work significantly better in the field than traditional approaches, especially when we are talking about Mensural Notation. These machine learning researches have taken on the task of recognizing Mensural Notation as another written text recognition task, but have not explored the characteristics of musical elements in depth. Other works have tried to dig deeper into analyzing musical elements and the extraction of their characteristics, but at a segmented symbol level, without reflecting this in a complete recognition environment or with an established dataset. In this paper, we will try to make a complete recognition system directly from the scores, using techniques that enhance information obtained from symbols. We explore language interpretations for improving results on a publicly available dataset. In our experiments, we have made a 32\% improvement in regards to error at the symbol level. With this, we have gone from a 5.11\% error rate, using the same technology as the latest approaches, to a 3.48\% error rate, as calculated using language reinterpretations. TFGM