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...

Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting whic...

Handwritten character recognition is the ability of a Computer to receive and interpret handwritten input. Computers may find difficultly in deciphering many kinds of printed characters which is of different fonts and styles or handwritten characters. This paper focuses on the recognition of handwritten Malayalam characters. The proposed system consists of image acquisition, preprocessing, segm...

Computer handwriting recognition ooers a new way of improving the human-computer interface and of enabling computers to read and process the many handwritten documents that must currently be processed manually. This thesis describes the design of a system that can transcribe handwritten documents. First, a review of the aims and applications of computer handwriting recognition is presented, fol...

During the last two decades significant work has been reported in field of cursive language’s recognition especially, Arabic, Urdu and Persian languages. The unavailability such Pashto language is because of: absence a standard database research that ultimately acts as big barrier for community. slight change characters’ shape an additional challenge researchers. This paper presents efficient O...

Handwritten recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, photographs, touchscreens and other devices. In this paper we will using three (3) classification to recognize the handwritten which is SVM, KNN and Neural Network. Keywords—Handwritten recognition; SVM; K-Nearest Neighbor; Neural Network;

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