# Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes
## 1 Introduction  
There are different biometric features that allow the verification or identification of people, among them is writing. The rhythm of writing, which is unrepeatable and unique, captures particular graphic characteristics in the text which allow the identification of the author. People recognition through the analysis of handwritten texts is widely used in different tasks such as identifying authorship, detecting forgeries, fraud, threats and theft, in documents of different types such as holographic wills, letters, checks, and so forth [1]. Most state-of-the-art works analyze complex text structures to extract features, such as full pages, text and paragraphs [2; 3; 4; 5; 6], words [7; 8; 9] and signatures [10; 11; 12]. Working with very complex sources in order to obtain a high verification ratio results in complexity throughout the entire processing sequence: developing sophisticated segmentation algorithms for the region of interest, complexity in the automatic computation of descriptors to represent the original data with low dimensionality and high execution times for the algorithms. Contrary to the more traditional literature characterized by the complexity of the structures used, a new approach begins to consider simple elements of handwritten text to solve the problem of writer verification. Along these lines, in Reference [13] a new database is proposed containing 6 remarkably simple grapheme types: \"e\" \"S\", \"\(\cap\)\", \"C\", \"\(\sim\)\"and\" U\". In addition, a new descriptor is introduced to represent the texture of the handwritten strokes (relative position of the minimumgray value points within the stroke) and successful verification tests are performed with a Support Vector Machine (SVM) based classifier. In Reference [14], it is proposed to represent the texture of simple graphemes by means of B-Spline transformation coefficients and classifiers based on banks of SVMs. In Reference [15], the character \"e\" is excluded because it presents crosses in its structure, which generates complexity in the computation of descriptors, the Local Binary Patterns (LBP) are introduced to represent the surface of the simple graphemes, and a classifier based on SVM is built. Recently, in Reference [16], it was proposed to simplify the structure of the classifier and reduce training time using Neural Networks of Extreme Learning (ELM). In the aforementioned works, preprocessing and transformation of the original image are performed, descriptors representing the surface texture of the grapheme are computed, and classifiers are constructed for the verification of the writer. In order to simplify the pipeline of simple graphemes processing, without to perform pre-processing (working directly with the original image), without to compute descriptors, and to achieve a high rate of writer identification accuracy, this paper proposes to analyze the image of the Simple Grapheme using Convolutional Neural Networks (CNN). The advantages of this approach are as follows:  
* Directly working with the original image without making any transformations. * Biometric features are obtained automatically through CNN filters. * The use of CNN allows a high success rate in the test set because the constructed classifiers correspond to highly non-linear transformations. * There are consolidated frameworks for the implementation of CNN networks [17; 18], which use high-performance computing techniques (multi-core and GPUs) to reduce network training time. In this work, experiments are performed with the network models AlexNet [19], VGG (VGG-16 and VGG-19) [20] and ResNet (ResNet-18) [21]. AlexNet and VGGs networks can be considered classic convolutional neural networks, as they follow the basic serial connection scheme, that is, a series of convolutions, pooling, activation layers and finally some completely connected classification layers.