# Short Words for Writer Identification Using Neural Networks
###### Abstract
The paper aims at identifying handwritten Farsi digits written in different handwritten styles. Digits are classified using several traditional methods, including K-nearest neighbor, artificial neural network, and support vector machine (SVM) classifiers. New features of digits, namely, geometric and correlation-based features, have been demonstrated to achieve better recognition performance. Furthermore, a new dataset of Arabic letters written exclusively by children aged 7-12, which is called Hijja, is presented in [13]. The dataset contains 47,434 characters written by 591 participants. An automatic handwriting recognition model based on CNNs is proposed. Some sets of simple-shaped geometric features are used for achieving the offline verification of signatures in [14]. These features include baseline slant angle (BSA), aspect ratio (AR), normalized area (NA), the center of gravity, as well as the line's slope that joins the center of gravities of the signature's image two splits. The similarity measure within the feature space between the two signatures is determined using the Euclidian distance. Several modern techniques for neural network training and recognition enhancement are based on their structures' symmetry [15]. Such approaches demonstrate impressive results, both for recognition practice and for understanding data transformation processes in various feature spaces. This survey examines symmetrical neural network architectures--Siamese and triplet. Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields [16]. This study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously and (b) utilizing the focal loss to deal with the extreme imbalance between positive and negative offline signatures. From the perspective of handwriting analysis, the authors of [17] exemplified and systematized general methods for an informal falsification of artificial neural networks applied to the verification of offline handwritten document authorship. The work in [18] proposes an approach to analyze the images of simple graphemes using CNNs. Recently, a few papers were published that propose methods for offline writer identification, which are different from the method proposed in the present work. Specifically, an offline system for text-independent writer identification of handwritten documents was presented in [19]. The system extracts a handwritten connected component contour, which in turn is divided into segments of a specific length. The system uses the proposed features to train a k-means clustering algorithm to construct a codebook of size \(K\). The method uses occurrence histograms of the extracted features in the codebook to create a final feature vector for each handwritten document. The work in [20] exploits the textural information in handwriting to characterize writers from historical documents. The authors use oBIF (oriented Basic Image Features) and hinge features and introduce a novel moment-based matching method to compare the feature vectors extracted from writing samples. Classification is based on the minimization of a similarity criterion using the proposed moment distance. Finally, a comparative study in [21] evaluates several deep learning architectures, such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet, end-to-end to examine their advantages over offline handwriting for writer identification problems with IAM and CVL databases. In the present work, writer verification is carried out based on short-length words and neural networks. We were inspired by the need that exists several times to recognize/distinguish a person only from one of his words that he may have left in a small note.