Automatic writer identification using connected-component contours and edge-based features of uppercase Western script.
In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO3s) in uppercase handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the uppercase character set. Using a codebook of CO3s from an independent training set of 100 writers, the probability-density function (PDF) of CO3s was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO3 PDF for identifying individual writers on the basis of a single sentence of uppercase characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the CO3 PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.
Schomaker
Lambert
L
AI Institute, Groningen University, Grote Kuisstraat 2/1, Groningen, The Netherlands. schomaker@ai.rug.nl
Bulacu
Marius
M
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D019366:Western World