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Clinical de-identification aims to identify Protected Health Information in clinical data, enabling data sharing and publication. First automatic de-identification systems were based on rules or on machine learning methods, limited by language changes, lack of context awareness and time consuming feature engineering. Newer deep learning techniques for sequence labeling have shown better results with a reduction in feature engineering efforts and the use of word representation techniques in vector space. However, they are not able to jointly represent the polysemic and context-dependent nature of words, as well as their morpho-syntactic mutations characteristic of handwriting. To address these limitations, a new de-identification approach based on deep learning techniques for Named Entity Recognition has been proposed, whose key factors are: (i) a Bidirectional Long Short-Term Memory + Conditional …