Evaluating structural pattern recognition for handwritten math via primitive label graphs

Richard Zanibbi; Harold Mouchère; Christian Viard-Gaudin

doi:10.1117/12.2008409

4 February 2013 Evaluating structural pattern recognition for handwritten math via primitive label graphs

Richard Zanibbi, Harold Mouchère, Christian Viard-Gaudin

Proceedings Volume 8658, Document Recognition and Retrieval XX; 865817 (2013) https://doi.org/10.1117/12.2008409
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

Citation Download Citation

Richard Zanibbi, Harold Mouchère, and Christian Viard-Gaudin "Evaluating structural pattern recognition for handwritten math via primitive label graphs", Proc. SPIE 8658, Document Recognition and Retrieval XX, 865817 (4 February 2013); https://doi.org/10.1117/12.2008409

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