Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer's gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans

Influence of codebook patterns on writer recognition: An experimental study

Codebook-based writer characterization is an effective technique that has been investigated in a number of recent studies on identification and verification of writers. These methods divide a set of writing samples into small units (fragments or graphemes) and cluster these patterns to produce a codebook. Writer of a handwritten sample is then characterized by the probability (distribution) of producing the codebook patterns. In most cases, a small subset of the database under study is employed to produce the codebook while the rest of the database is used in evaluations. This work aims to validate the hypothesis that the codebook simply serves as a representation space to compare different writings and, in most cases, the patterns in the codebook do not significantly influence the identification and verification performance. The hypothesis is validated by generating a number of codebooks using Greek, Arabic and Chinese handwritten samples. Moreover, codebooks using fragments of handwritten music scores, printed text and synthetic data are also investigated. Evaluations on three well-known handwriting databases (CVL, BFL and IAM) validate the idea that, in general, the codebook patterns do not have a significant impact on characterizing writer from handwriting.Scopu

Signature Verification for Offline Skilled Forgeries Using Textural Features

This study explores the effectiveness of two texturalmeasurements on signature verification for skilled forgeries. These texture features include 2D autoregressive coefficients andrun-length distributions. Signature images corresponding to 521writers from the GPDS960 database were used to evaluate theperformance of these features. Comparison of the proposedtextural features with a number of state-of-the-art featuresrealized interesting results. The run-length features outperformother features for a sufficient number of genuine signatures inthe training dataset.Scopu