The Shadoc team takes over from the Intuidoc team. Like Intuidoc, Shadoc focuses on modelling man-madedata for written communication: handwriting, gesture (2D and 3D), and documents, under various aspects: analysis, recognition, composition, interpretation.
The objective is to achieve a continuum between paper and digital documents with a certain readability. We mainly focus on the following topics:
- Intelligent recognition of handwritten content: documents, writings, gestures;
- Analysis of the semantic/structural content: document structure, stages of production of diagrams,drawings, musical scores, sketches, architectural plans;
- Design of new AI, combining recognition and analysis: offer enriched experiences for digital humanitiesor e-education.
The roadmap of the Shadoc team is on the frontier of several research axes: Pattern Recognition, MachineLearning, Artificial Intelligence, Human-Machine Interaction, Uses and Digital Learning.
Our research is characterized by the hybridization of several AI approaches: two-dimensional grammars,deep learning, fuzzy inference systems… This hybridization aims at guaranteeing, beyond performance,important aspects such as: explicability, genericity, adaptability, data frugality.
Beyond hybridization, the originality of this research is to focus on user interaction. This strategy aims atanswering the limits of the current approaches which are based on non-interactive treatments. The conceptis to reinforce the decision processes by relying on the implicit validations or explicit corrections of a userto avoid the propagation of errors throughout the analysis. The notions of interpretation, adaptation andincremental learning are at the heart of this research, the objective being to design efficient, robust andself-evolving systems.
