Offline Handwritten Text Images for Gender Prediction

One of the most consequential creations in the human evolution phase is handwriting. Due to writing, today we are conveying our reflections, making business pacts, rendering an understandable world and making hitherto tasks austerer. Determining gender using offline handwriting is an applied research problem in forensics, psychology, and security applications, and with technological evolution, the need is growing. The general problem of gender detection from handwriting poses many difficulties resulting from interpersonal and intrapersonal differences. A major one is a need for more data which we aim to curb with this dataset. This dataset includes handwritten text samples in Hindi and English from 170 people, of which 137 are men and 33 are women. Each sample contains seven handwritten text images, including a number, quotes, college names, and a person's name in both languages. These images contain various text forms by the same user, which is necessary for robust and effective gender detection from offline handwritten texts. This makes an aggregate of 1190 hand-collected images. This dataset aims to develop an automated gender classification system, which can help create a real-world impact.

在人类进化阶段，手写是其中最具深远意义的创举之一。得益于书写，我们如今能够传达思想、缔结商业契约、构建易于理解的世界，并将昔日繁重的工作变得更为简练。利用离线手写文字进行性别判定，是法医学、心理学及安全领域的一项应用研究课题，随着技术的进步，这一需求正日益增长。手写性别识别的一般问题，因其人际及个体差异而呈现出诸多困难。其中之一便是数据量的需求，而我们希望通过本数据集来缓解这一需求。本数据集包含了来自170人的手写文本样本，其中男性137人，女性33人。每个样本包含七个手写文本图像，包括数字、引语、大学名称以及两种语言中的人物姓名。这些图像包含了同一用户的各种文本形式，这对于离线手写文本的稳健且有效的性别识别至关重要。这总计构成了1190张手工收集的图像。本数据集旨在开发一种自动性别分类系统，以期为现实世界带来实际影响。