Ink Classification Using Convolutional Neural Network
Ink classification plays a vital role in document forgery detection and analysis. Hyperspectral imaging (HSI) is an emerging tool for non-invasive analysis of materials in various fields including forensic analysis. HSI records hundreds of narrowband images in electromagnetic spectrum and this will produce larger data sizes compared to conventional imaging techniques. In order to handle this large information, it is necessary to develop some automated and more computationally efficient methods for data analysis. This paper presents the deep learning approach for hyperspectral ink classification by using one-dimensional (1D) Convolutional Neural Network (CNN). In addition to that, we have compared the CNN results against two standard hyperspectral classification methods; Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID). The results from classification algorithms revealed the effectiveness of CNN for ink classification with respect to the two conventional methods used.