Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.

A new scheme for off-line handwritten connected digit recognition

Abstract: A scheme is proposed for off-line handwritten connected digit recognition, which uses a sequence of segmentation and recognition algorithms. First, the connected digits are segmented by employing both the gray scale and binary information. Then, a new set of features is extracted from the segments. The parameters of the feature set are adjusted during the training stage of the hidden Markov model (HMM) where the potential digits are recognized. Finally, in order to confirm the preliminary segmentation and recognition results, a recognition based segmentation method is presented.

Handwritten digit recognition based on multi-classifier combination

Abstract: A handwritten digit recognition method based on multi-classifier combination is described in the paper. First I an objective parameter called performance parameter is defined to judge the classifier's performance. Then, a new combination algorithm of multi-classifier is presented. Some properties of combination method are also given in this paper. The Concordia University CENPARMI handwritten digit database is used in the experiment. Nine classifiers which use different features are combined to recognize the image. The experiment results (Recognized,Rejected and Reliability) are 97.05%, 2.05%, 99.08% respectively.

Character Segmentation of Hindi Unconstrained Handwritten Words

Abstract: The proper character level segmentation of printed or handwritten text is an important preprocessing step for optical character recognition OCR. It is noticed that the languages having cursive nature in writing make the segmentation problem much more complicated. Hindi is one of the well known language in India having this cursive nature in writing style. The main challenge in handwritten character segmentation is to handle the inherent variability in the writing style of different individuals. In this paper, we present an efficient character segmentation method for handwritten Hindi words. Segmentation is performed on the basis of some structural patterns observed in the writing style of this language. The proposed method can cope with high variations in writing style and skewed header lines as input. The method has been tested on our own database for both printed and handwritten words. The average success rate is 96.93i¾?%. The method yields fairly good results for this database comparing with other existing methods. We foresee that the proposed character segmenattion technique can be used as a part of an OCR system for cursive handwritten Hindi language.

Printed and Handwritten Character & Number Recognition of Devanagari Script using Gradient Features

Abstract: In this paper we are extracting feature of handwritten and ISM printed characters of devanagri script. we are extracting Gradient feature of the devanagari script ,for that we are using two operators i.e. Sobel and Robert operator respectively . Here we are computing gradient in 8,12,16,32 directions and getting different feature vectors respectively. We are using each directional vector separately for classification.

Learning and Application of Differential Grammars

Abstract: We examine the Differential Grammar , a representat ion designed to discr iminate which of a set of eonfusable al ternat ives is most likely in the context it occurs in. This approach is useful whereever uncer ta inty may exist about the ident i ty of a token or sequence of tokens, including in speech recognition, optical character recognition and machine t ransla t ion. In this paper our appl ica t ion is word processing: we discuss mul t ip le models of confusion which may be used in the identification of confused words, we show how significant contexts may be identified and condensed into Differential Grammars , and we contrast the performance of our implementa t ion with tha t of two commercial g r ammar checkers which purpor t to handle the confused word problem.