title: Markov logic networks for optical chemical structure recognition.
abstract: Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo.unifi.it.
authors: Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Firenze , Via di Santa Marta, 3, 50139 Firenze, Italy.
authors: Paolo
authors: P
authors: Frasconi
authors: Francesco
authors: F
authors: Gabbrielli
authors: Marco
authors: M
authors: Lippi
authors: Simone
authors: S
authors: Marinai
mesh_terms: D003196:Computer Graphics; D062126:Databases, Chemical; D007091:Image Processing, Computer-Assisted; D008390:Markov Chains; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*; D054852:Small Molecule Libraries / Q000737:chemistry*; D012984:Software
chemical_list: D054852:Small Molecule Libraries