This thesis presents new approaches in off-line Arabic Handwriting Recognition based on conventional Bernoulli Hidden Markov models. Until now, the off-line handwriting recognition, in particular, the Arabic handwriting recognition is still far away form being perfect. Hidden Markov Models (HMMs) are now widely used for off-line handwriting recognition in many languages and, in particular, in A...

Offline handwriting recognition is usually performed by first extracting a sequence of features from the image, then using either a hidden Markov model (HMM) [9] or an HMM / neural network hybrid [10] to transcribe the features. However a system trained directly on pixel data has several potential advantages. One is that defining input features suitable for an HMM requires considerable time and...

A new paradigm, which models the relationships between handwriting and topic categories (denoted as ‘concepts’), in the context of medical forms, is presented. The ultimate goals are (i) the recognition of medical handwriting, and (ii) the use of such information for a medical form search engine. Medical forms have diverse, complex and large lexicons consisting of English, Medical and Pharmacol...

Most of the dynamic information present in an on-line handwriting signal is often ignored by on-line handwriting recognition systems. It is shown here that the dynamic information is complementary to the shape information and may be used to improve the accuracy of the recognition system.

Historical manuscripts are one of documents that important to be preserved because they contain a lot information, example them is script as the historical . document mostly still use handwriting in so many reserch. Currently, there research regarding preservation characters. One way can used digitization process. Digitizing’s process tanable by recognizing existing information using technology...

Automated recognition of unconstrained handwriting continues to be a challenging research task. In addition to the errors caused by image quality, image features, segmentation, and recognition, in this paper we have also explored the influence of image complexity on handwriting recognition and compared humans’ versus machines’ recognition. We describe a new methodology that will exploit the gap...

Most of the dynamic information present in an on-line handwriting signal is often ignored by on-line handwriting recognition systems. It is shown here that the dynamic information is complementary to the shape information and may be used to improve the accuracy of the recognition system.

This paper proposes active handwriting models, in which kernel principal component analysis is applied to capture nonlinear handwriting variations. In the recognition phase, the chamfer distance transform and a dynamic tunnelling algorithm (DTA) are employed to search for the optimal shape parameters. The proposed methodology is successfully applied to a novel radical decomposition approach to ...

Reading comprehension is largely tested in schools using handwritten responses. The paper describes computational methods of scoring such responses using handwriting recognition and automatic essay scoring technologies. The goal is to assign to each handwritten response a score which is comparable to that of a human scorer even though machine handwriting recognition methods have high transcript...

Poor handwriting is a diagnostic criterion for developmental coordination disorder. Typical of poor handwriting is its low overall quality and the high variability of the spatial characteristics of the letters, usually assessed with a subjective handwriting scale. Recently, Dynamic Time Warping (DTW), a technique originally developed for speech recognition, was introduced for pattern recognitio...