The Dutch Text2Picto system (Sevens, 2018; Vandeghinste et al., 2015) aims to automatically translate text into pictographs for people with an intellectual disability in the context of Augmentative and Alternative Communication (AAC). The AAC technologies are used by disabled people to help them to communicate in daily life and to be more independent in their interactions with others improving the social inclusion (Beukelman and Mirenda, 1998). In the framework of the Able to Include European project, the Text2Picto tool has been designed for three source languages (Dutch, English, Spanish) and two target pictographic languages (Sclera, Beta). In this communication, we propose two main contributions: (1) to adapt th is system to French and (2) to extend it to a third pictograph set, namely Arasaac, that becomes more and more popular within AAC. We describe how we automatically linked the pictographs and their metadata to synsets of two French WordNets (WOLF and WoNeF) so that the translation engine uses the semantic relations between concepts. In order to preprocess the source text in French, we also have adapted the shallow linguistic analysis carried out in Text2Picto to French. Finally, we evaluated our system, whose parameters were optimized using a hill climbing algorithm, on three corpora representing several use cases: social media, books for children, and medical communication between doctors and patients. The results of our experiments are in line with those of the Dutch Text2Picto for social media and of a similar text to picto system for French children's books (Vaschalde et al., 2018). For medical use case, we also carried out a manual evaluation. As a result of our experiments, we present a fully fledged sy stem easily extensible to other languages and we discuss the challenges we are faced with when evaluating these technologies for adaptation to other use cases.