Chang, Samuel Hsiao-HengPlimmer, BerylBlagojevic, RachelMarc Alexa and Ellen Yi-Luen Do2014-01-282014-01-282010978-3-905674-25-51812-3503https://doi.org/10.2312/SBM/SBM10/095-102While many approaches to digital ink recognition have been proposed, most lack flexibility and adaptability to provide acceptable recognition rates across a variety of problem spaces. Time and expert knowledge are required to build accurate recognizers for a new domain. This project uses selected algorithms from a data mining toolkit and a large feature library, to compose a tailored software component (Rata.SSR) that enables single stroke recognizer generation from a few example diagrams. We evaluated Rata.SSR against four popular recognizers with three data sets (one of our own and two from other projects). The results show that it outperforms other recognizers on all tests except recognizer and data set pairs (e.g. PaleoSketch recognizer and PaleoSketch data set) in these cases the difference is small, and Rata is more flexible. We hence demonstrate a flexible and adaptable procedure for adopting existing techniques to quickly generate accurate recognizers without extensive knowledge of either AI or data mining.Categories and Subject Descriptors (according to ACM CCS): I.7.5 [Document and Text Processing]: Graphics recognition and interpretation, I.2.10 [Artificial Intelligence]: Shape, I.5.2 [Pattern Recognition]: Classifier design and evaluation.