Markov Logic Networks for Optical Chemical Structure Recognition
datasetposted on 2014-08-25, 00:00 authored by Paolo Frasconi, Francesco Gabbrielli, Marco Lippi, Simone Marinai
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.
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chemical exchange formatMarkov Logic Networksnovel approachMarkov logicBasic entitiesconnection tableprocessing moduleimage data setsweb serverbitmap imagepattern recognition techniquespipelined integrationreasoning engineknowledge representationOptical Chemical Structure RecognitionOptical chemical structure recognitionchemical structure formulaproblem