# Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes
## 5 Conclusions  
In this work, a scheme for processing simple graphemes for writer identification is presented. The approach is based on the use of convolutional neural networks. The experimentation considered the image of rectified grapheme (traditional representation of simple graphemes) and the image of the original grapheme. The AlextNet, VGG-16, VGG-19 and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Descriptor** & **Classifier** & **“C”** & **“\(\sim\)”** & **“\(\cap\)”** & **“S”** & **“U”** & **Average** & **Grouped** \\ \hline RPofMGLP [13] & SVM & 97\% & 97\% & 97\% & 98\% & 97\% & 97\% & – \\ BSC-RPofMGLP [14] & SVM & 97\% & 97\% & 97\% & 98\% & 97\% & 97\% & – \\ GLofSK [15] & SVM & 83\% & 80\% & 82\% & 79\% & 83\% & 81\% & – \\ AGLofSPL [15] & SVM & 98\% & 98\% & 98\% & 98\% & 98\% & 98\% & – \\ WofGra [15] & SVM & 96\% & 93\% & 96\% & 92\% & 94\% & 94\% & – \\ LBPofGra [15] & SVM & 98\% & 98\% & 98\% & 100\% & 98\% & 98\% & – \\ LBPofGra [16] & ELM & 91\% & 93\% & 91\% & 91\% & 92\% & 92\% & 90\% \\ LBPofGra [16] & ML-ELM & 95\% & 96\% & 96\% & 95\% & 95\% & 96\% & 92\% \\ \hline \multirow{3}{*}{Original Grapheme} & AlexNet & 95\% & 96\% & 95\% & 95\% & 96\% & 96\% & 98\% \\  & VGG-16 & 97\% & 98\% & 95\% & 98\% & 97\% & 97\% & 98\% \\ \cline{1-1}  & VGG-19 & 96\% & 96\% & 95\% & 98\% & 97\% & 96\% & 98\% \\ \cline{1-1}  & ResNet-18 & 97\% & 97\% & 96\% & 96\% & 97\% & 97\% & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison respect to other approaches. ResNet-18 models have been adopted, due to the fact that they present an adequate compromise between accuracy and training time. The best results have been obtained with the original grapheme image and ResNet-18 Neural Network, considering the accuracy and time trade-off. Using ResNet-18, an average hit-rate of 97% has been achieved considering individual graphemes, and 98% of hit-rate considering grouped graphemes. The results show a high level of performance of the original grapheme, without the need to transform the image or compute specific descriptors, drastically reducing the complexity of the simple grapheme processing chain. M.M. Conceptualization, methodology, software, J.N.-T. and V.A. ; software, M.M., J.N.-T. and V.A. ; formal analysis, M.M., J.N.-T. and V.A. ; investigation, M.M., J.N.-T. and V.A. ; writing--original draft preparation, M.M., J.N.-T. and V.A. ; writing--review and editing, M.M. and J.N.-T; project administration, M.M. ; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript. This research was funded by Innovation Fund for Competitiveness--FIC, Government of Maule, Chile--Project Transfer Development Equipment Estimation Quality of Raspberry, code 40.001.110-0 (Esta investigation for financiala por el Fondo de Innovacion para la Competitividad--FIC, Gobierno de Maule, Chile--Proyecto de Transferencia de Desarrollo de Equipo de Estimacion de Calidad de la Frambuesa, codigo 40.001.110-0). The authors thank the Laboratory of Technological Research in Recognition of Patterns (www.litrp.cl) of the Universidad Catolica del Maule, Chile, for providing the computer servers where the experiments have been carried out. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: