In the last decade, significant, largely-governmental funding has been applied to the automatic transcription of handwritten documents. Uses for this kind of technology are somewhat limited given that the numbers of handwritten documents are on the decline. However, certain types of handwritten historical records can be crucial for genealogical research in that they identify key vital facts. In...

We present in this paper a system of English handwriting recognition based on 26-point feature extraction of the character. Basically an off-line handwritten alphabetical character recognition system using multilayer feed forward neural network has been described in our work. Firstly a new method, called, 26-point feature extraction is introduced for extracting the features of the handwritten a...

This paper presents an effective automated analysis system for mixed documents consisting of handwritten texts and graphic images. In the preprocessing step, an input image is binarized, then graphic regions are separated from text parts using chain codes of connected components. In the character recognition step, we recognize two different sets of handwritten characters: Korean and alphanumeri...

Free-form online handwritten documents contain a high diversity of content, organized without constraints imposed to the user. The lack of prior knowledge about content and layout makes the modeling of contextual information of crucial importance for interpretation of such documents. In this work, we present a comprehensive investigation of the sources of contextual information that can benefit...

The aim of the tranScriptorium project is to produce software applications that will ‘read’ historical handwritten documents and will produce transcripts for these documents. There are many millions of pages of handwritten documents, the basis for much research in the arts and humanities, and yet the vast majority of this material—including manuscripts that have been digitized—remains inaccessi...

An algorithm and a system for searching handwritten Arabic documents to locate key words is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary lookup, locates word images in an indexed (segmented) ...

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. The performance of information retrieval algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform and Speeded Up Robust Features, in order to represent handwritten words in...

Off-line recognition of text play a significant role in several application such as the automatic sorting of postal mail or editing old documents. It is the ability of the computer to distinguish characters and words. Automatic off-line recognition of text can be divided into the recognition of printed and handwritten characters. Off-line Arabic handwriting recognition still faces great challen...

This paper presents a novel holistic technique for classifying Arabic handwritten text documents, which it is performed in several steps. First, the Arabic handwritten document images are segmented into their connected parts. A simple heuristic segmentation algorithm is used which finds segmentation points in printed and cursive handwritten words. Second, several features are extracted from the...

Hidden Markov Models constitute an established approach often employed for offline handwritten character recognition in digitized documents. The current work aims at evaluating a number of procedures frequently used to define features in the character recognition literature, within a common Hidden Markov Model framework. By separating model and feature structure, this should give a more clear i...