Non-english and non-latin signature verification systems: A survey

Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems

Digital Paleography: Using the Digital Representation of Jawi Manuscripts to Support Paleographic Analysis

Palaeography is the study of ancient handwritten manuscripts to date the age and to localize ancient and medieval scripts. It also deals with analysing the development of the letters shape. Ancient Jawi manuscripts are one of the least studiedarea. Nowadays, over 7789 known Jawi manuscripts are kept in custody of various libraries in Malaysia. Most of these manuscripts were undated with unknown authors and location of origin. Analysing the different types of writing styles and recognizing the manuscript illuminations can discover this important information. In this paper, we discuss the palaeographical analysis from the perspective of computer science and propose a general framework for that. This process involves investigation of Arabic influence on the Jawi manuscript writings, establishing the palaeographical type of the script, and classification of writing styles based on local and global Jawi image features

Digital Paleography: Using the Digital Representation of Jawi Manuscripts to Support Paleographic Analysis

Palaeography is the study of ancient handwritten manuscripts to date the age and to localize ancient and medieval scripts. It also deals with analysing the development of the letters shape. Ancient Jawi manuscripts are one of the least studiedarea. Nowadays, over 7789 known Jawi manuscripts are kept in custody of various libraries in Malaysia. Most of these manuscripts were undated with unknown authors and location of origin. Analysing the different types of writing styles and recognizing the manuscript illuminations can discover this important information. In this paper, we discuss the palaeographical analysis from the perspective of computer science and propose a general framework for that. This process involves investigation of Arabic influence on the Jawi manuscript writings, establishing the palaeographical type of the script, and classification of writing styles based on local and global Jawi image features

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards

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