Investigating Jaccard Distance similarity measurement constriction on handwritten pen-based input digit

This paper presents a preliminary study on conventional Jaccard Distance performance in recognizing shapes with and without pre processing tasks. This study also identified the pre processing tasks that should be conducted to improve recognition performance. Jaccard Distance is performed by measuring the asymmetric information on binary variable and the comparison between vectors component. It compared two objects and notified the degree of similarity of these objects. This study also evaluated recognition performance on hand writing on isolated digits written using pen-based device. The first part of the work showed low recognition performed by the conventional Jaccard Distance when there was no pre processing task done onto the input. However, after translation, rotation, invariance scale content and noise resistance were added it showed improvement on the recognition progress. However the improvement was not very significance. Result showed that the degree of accuracy only improve averagely by 20%. Therefore thorough pre-processing tasks are to be carried out for the pen-based input using Jaccard Distance measurement.

[1]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[2]  Yang Mingqiang,et al.  Shape Matching and Object Recognition Using Chord Contexts , 2008, 2008 International Conference Visualisation.

[3]  Cristina Urdiales,et al.  Planar shape indexing and retrieval based on Hidden Markov Models , 2002, Pattern Recognit. Lett..

[4]  H. Nemmour,et al.  New Jaccard-Distance Based Support Vector Machine Kernel for Handwritten Digit Recognition , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[5]  S.M. Hazarika,et al.  Enhanced Shape Context for Object Recognition , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[6]  N. Nehra,et al.  Trust Aware Rouitng with Load Balancing in Ad Hoc Network Using Mobile Agent , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[7]  Yan Zhang,et al.  On the Euclidean distance of images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Cristina Urdiales,et al.  A Hidden Markov Model object recognition technique for incomplete and distorted corner sequences , 2003, Image Vis. Comput..

[9]  Chaur-Chin Chen,et al.  Similarity Measurement Between Images , 2005, COMPSAC.

[10]  Xiaofeng Wang,et al.  Shape recognition based on neural networks trained by differential evolution algorithm , 2007, Neurocomputing.

[11]  Shigeki Sagayama,et al.  Pen pressure features for writer-independent on-line handwriting recognition based on substroke HMM , 2002, Object recognition supported by user interaction for service robots.

[12]  Jun Guo,et al.  Efficient Computation of Mahalanobis Distance in Financial Hand-Written Chinese Character Recognition , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[13]  Hua Li,et al.  Similarity Measurement Based on Trigonometric Function Distance , 2006, 2006 First International Symposium on Pervasive Computing and Applications.

[14]  Beiji Zou,et al.  Shape-Based Trademark Retrieval Using Cosine Distance Method , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[15]  Mohammad Reza Daliri,et al.  Robust symbolic representation for shape recognition and retrieval , 2008, Pattern Recognit..