Off-line Text-independent Writer Identification Using Local Convex Micro-Structure Patterns

Feature extraction is fundamental in writer identification to characterize a given handwritten text by a set of discriminative measures, which are used to capture the large variability between handwritten samples. This paper presents an effective learning-based approach for off-line text-independent writer identification using Local Convex Micro-Structure Patterns (LCxMSP) descriptor. The LCxMSP algorithm, which characterizes the writing style of each writer, is applied to a set of connected component sub-images extracted from the handwriting samples (documents or set of words/text lines). Histogram features computed from all the labeled components in all the writing samples are fed to the 1NN (Nearest Neighbor) classifier in order to identify the writer of the tested handwritten samples. Experimental results show that our proposed system combined with the LCxMSP descriptor demonstrate superior performance on the Hybrid-language ICDAR2011 database compared to the state-of-the-art writer identification systems.