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 simple and efficient optical character recognition system for basic symbols in printed Kannada text

Abstract: Optical Character Recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian languages. Efforts are on the way for the development of efficient OCR systems for Indian languages, especially for Kannada, a popular South Indian language. We present in this paper an OCR system developed for the recognition of basic characters (vowels and consonants) in printed Kannada text, which can handle different font sizes and font types. Hu’s invariant moments and Zernike moments that have been progressively used in pattern recognition are used in our system to extract the features of printed Kannada characters. Neural classifiers have been effectively used for the classification of characters based on moment features. An encouraging recognition rate of 96.8% has been obtained. The system methodology can be extended for the recognition of other south Indian languages, especially for Telugu.

Diagonal based feature extraction for handwritten character recognition system using neural network

Abstract: An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.

Real-time Arabic handwritten character recognition

Abstract: This paper considers the development of a real-time Arabic handwritten character recognition system. The shape of an Arabic character depends on its position in a given word. The system assumes that characters result from a reliable segmentation stage, thus, the position of the character is known a priori. Four different sets of character shapes have been independently considered. Each set is further divided into four subsets depending on the number of strokes in the character. The system has been heavily tested and the average recognition rate has been found to be 99.6% where all the misrecognized characters were actually written with little care. The system can be reliably used for the recognition of on-line handwritten characters entered via a graphic tablet. >

Transformation Invariant Autoassociation with Application to Handwritten Character Recognition

Abstract: When training neural networks by the classical backpropagation algorithm the whole problem to learn must be expressed by a set of inputs and desired outputs. However, we often have high-level knowledge about the learning problem. In optical character recognition (OCR), for instance, we know that the classification should be invariant under a set of transformations like rotation or translation. We propose a new modular classification system based on several autoassociative multilayer perceptrons which allows the efficient incorporation of such knowledge. Results are reported on the NIST database of upper case handwritten letters and compared to other approaches to the invariance problem.