# Characterization of Prints Based on Microscale Image Analysis of Dot Patterns
## 6 Conclusions  
In this paper, we investigated a promising solution based on a pattern recognition process to identify a print document (original) from a reprint document (copy or fake). We demonstrated that the geometrical shape of printed dots can be analyzed at microscopic scale to differentiate a print from a reprint, as a fingerprint. To improve the efficiency of the dot segmentation process, we propose a new image processing pipeline. To better characterize complex geometrical dot shapes, we defined six types of dot patterns, including four types of doughnut patterns, and defined a new rules-based characterization method which combines several dot pattern features. To better characterize each printed sample from the set of dots printed on it, we proposed a new statistical analysis method based on a pattern recognition process. The preliminary experiments demonstrated the relevance of the criteria/rules/methods proposed and of the statistical analyze method implemented. In a future work, we will perform complementary experiments to study the robustness of our solution in the function of the printing parameters, as well as the printing device used. We will also study the impact of the scan process involved in the reprint process (based on a scan-and-print process). The implemented solution suffers from some limitations that were discussed in the paper. In future work we will study how to improve some steps of the image processing process, especially the segmentation process when neighboring dots are overlapping or when color dots (primary colors) are overlapping. If one want to generalize the rules suggested in this paper for new data without overfitting, it could be interesting to investigate an unsupervised machine learning method [14]. Noise learning is one important cause of overfitting. Pruning could be used to eliminating less meaningful or irrelevant data and, finally, to prevent overfitting and to improve the classification accuracy [15]. During the learning process, stopping criteria could be used to determine when to stop adding conditions to a rule, or adding rule to a model description. Pruning could be also used to test the significance of rules based on significant differences between the distribution of positive and negative examples. It will be interesting to investigate if other combinations of rules could improve the classification accuracy of dots regardless of their shape. Parameter tuning is another important cause of overfitting; it needs sufficient samples for learning. To expand the training set, few (data augmentation) strategies could be used: acquire more training data; add some random noise to existing dataset; reacquire some data from existing data set through some image processing; produce some new data based on the distribution of an existing data set or from simulated data [15]. Well-tuned parameters make a good balance between training accuracy and regularity [15]. As the number of dot patterns resulting from the rotogravure printing process is very limited, the number of useful features will not increase with new data. However, the weight of these features could have an influence on the final classification. A regularization process could be used to minimize the weights of the features which have little influence on the final classification. Conceptualization/methodology/formal analysis, A.T.; Software/investigation/resources, A.T. and LD. ; Validation, I.D. and S.B. ; Writing--original draft preparation, A.T.; Writing--review and editing, I.D. and S.B. ; Supervision/project administration/funding acquisition: A.T. and S.B. All authors have read and agreed to the published version of the manuscript. This work developed in the frame of the project NO. IFC/7127-PACKMARK was funded by the Indo-French Centre for the Promotion of Advanced Research (IFCPAR/CEFIPRA). In this study, all print samples were designed by the authors and images of these samples were captured by them. All samples were printed thanks to the help of Sergusa Solutions Pvt. Ltd.