DCWI: Distribution descriptive curve and Cellular automata based Writer Identification

Abstract

Writer identification is an active area of research owing to its applications in a wide variety of fields, ranging from ancient document analysis to modern forensic document analysis. It deals with the writing style of documents and the learning of the discriminating features of different writers. In the domain of pattern recognition, the extraction of discriminative features of different writers has become very challenging. In order to address this concern, this work highlights a distribution descriptive curve (DDC-) and cellular automata (CA-) based model approach. The DDC utilizes the idea of the pixel distribution of handwritten text images to generate a unique curve as a feature vector. The generated feature vector is then fed to a support vector machine (SVM) as an input to identify the writer. Simultaneously, in a parallel mode, the initial handwritten text images are processed repeatedly with CA to generate another set of feature vectors. This new set of generated feature vectors are fed to a similarity-based classifier (SBC) as an input. The writer is predicted on the basis of the similarity of the features. The results from both of the approaches (DDC + SVM and CA + SBC) are merged to improve the performance of the model. This proposed model, DCWI, has good writer-identification capabilities compare with state-of-the-art techniques. Eventually, writer identification is accomplished using the ranking-based score scheme. The proposed model is evaluated on different datasets, e.g., IAM for English, IFN/ENIT for Arabic, and Kannada and Devanagari (Hindi) for the Indic script. The results show that the proposed model has better performance compared with existing state-of-the-art techniques.