Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Probably the single most important identifier is personal names. However, names prone to enumeration and errors although modern methods designed handle such challenges these sources of critical should be minimized. For this purpose, improved large-sca...

Despite the transition to digital information exchange, many documents, such as invoices, taxes, memos and questionnaires, historical data, answers exam questions, still require handwritten inputs. In this regard, there is a need implement Handwritten Text Recognition (HTR) which an automatic way decrypt records using computer. Handwriting recognition challenging because of virtually infinite n...

This paper proposes a copyright protection system using multimodal biometrics for content protection of digital images. Two biometric traits, one physiological (face) and one behavioral (offline handwritten signature) of the legitimate owner are used as watermarks. These watermarks are embedded at different levels of resolution to the high frequency wavelet coefficients through optimal control ...

OFFLINE handwriting recognition is the task of determining what letters or words are present in a digital image of handwritten text. It is of significant benefit to man-machine communication and can assist in the automatic processing of handwritten documents. It is a subtask of Optical Character Recognition (OCR), whose domain can be machine-print or handwriting but is more commonly machine-pri...

Learning Vector Quantisation (LVQ) is a method of applying the Vector Quantisation (VQ) to generate references for Nearest Neighbour (NN) classification. Though successful in many occasions, LVQ suffers from several shortcomings, especially the reference vectors are prone to diverge. In this paper, we propose a Classified Vector Quantisation (CVQ) to establish VQ for classification. By CVQ, eac...

We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when...