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.

Integrating knowledge sources in Devanagari text recognition system

Abstract: The reading process has been widely studied and there is a general agreement among researchers that knowledge in different forms and at different levels plays a vital role. This is the underlying philosophy of the Devanagari document recognition system described in this work. The knowledge sources we use are mostly statistical in nature or in the form of a word dictionary tailored specifically for optical character recognition (OCR). We do not perform any reasoning on these. However, we explore their relative importance and role in the hierarchy. Some of the knowledge sources are acquired a priori by an automated training process while others are extracted from the text as it is processed. A complete Devanagari OCR system has been designed and tested with real-life printed documents of varying size and font. Most of the documents used were photocopies of the original. A performance of approximately 90% correct recognition is achieved.

The role of meaning in word recognition.

Abstract: The goal of the present discussion is to bring into focus a number of implicit assumptions in the area of word recognition research (see also Seidenberg, this volume). These assumptions revolve around what will here be referred to as the magic moment in word processing. The magic moment refers to that point in time where the subject has recognized the word but has yet to access meaning. Researchers have argued that they can both collect data and develop adequate models of this crucial point in word processing. The outline for this chapter is as follows: First, the empirical support for a magic moment is evaluated. The thrust of this discussion is that the major tasks used to provide data regarding the magic moment entail characteristics that question their utility as pure reflections of this crucial point in word processing. Second, an alternative framework is presented that emphasizes the functional utility of words in language processing, that is, to convey meaning. Third, empirical evidence is presented that suggests that meaning can contribute to components involved in early word processing. Finally, there is a brief discussion of how meaning might be incorporated into the current theoretical accounts of word processing.

Handwritten English Character Recognition Using Neural Network

Abstract: Neural Networks are recently being used in various kind of pattern recognition. Handwritings of different person are different; therefore it is very difficult to recognize the handwritten characters. Handwritten Character recognition is an area of pattern recognition that has become the subject of research during the last some decades. Neural network is playing an important role in handwritten character recognition. Many reports of character recognition in English have been published but still high recognition accuracy and minimum training time of handwritten English characters using neural network is an open problem. Therefore, it is a great important to develop an automatic handwritten character recognition system for English language [1]. In this paper, efforts have been made to develop automatic handwritten character recognition system for English language with high recognition accuracy and minimum training and classification time. Experimental result shows that the approach used in this paper for English character recognition is giving high recognition accuracy and minimum training time.