Topic
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.
Papers published on a yearly basis
Papers
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TL;DR: It is reported that the winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high- resolution images.
Abstract: Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset (3) and the released Tesseract-OCR 3.02 system (1). We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images (78.80%). Index Terms—super resolution; optical character recogni- tion.
33 citations
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08 Jul 1991TL;DR: Preliminary experiments with computer simulation show that this approach is promising for both of the applications of the proposed model of selective attention, which has a function of segmenting patterns, as well as the function of recognizing patterns.
Abstract: Selective attention is one of the most essential mechanisms for visual pattern recognition. One of the authors had previously proposed a model of selective attention, which has a function of segmenting patterns, as well as the function of recognizing patterns. The idea of this selective attention model can be extended to be used for several applications. The structure of the model used for connected character recognition is discussed. The authors offer two examples of its applications. One is the recognition and segmentation of connected characters in cursive handwriting of English words. Another example is the recognition of Chinese characters. Preliminary experiments with computer simulation, in which only a small number of characters have been taught to the models, show that this approach is promising for both of the applications. >
32 citations
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TL;DR: Two methods of combining character recognition with techniques for retrieving Japanese documents are presented and it is shown how these methods can be applied to textual image retrieval.
32 citations
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03 Aug 2003TL;DR: An on-line handwritten Japanese text recognition method that is liberated from constraints on writing direction (line direction) and character orientation and employs writing-box-free recognition with context processing combined is described.
Abstract: This paper describes an on-line handwritten Japanese text recognition method that is liberated from constraints on writing direction (line direction) and character orientation. This method estimates the line direction and character orientation using the time sequence information of pen-tip coordinates and employs writing-box-free recognition with context processing combined. The method can cope with a mixture of vertical, horizontal and skewed lines with arbitrary character orientations. It is expected useful for tablet PCs, interactive electronic whiteboards and so on.
32 citations
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14 Aug 1995TL;DR: A way is shown to select the optimum values for some key parameters of the system to obtain minimum recognition error rates and to present here the effects of some parameters of this system on its performances and on its recognition errors.
Abstract: In this paper we describe a system that recognizes on-line Arabic handwriting characters. In this system, a dynamic programming algorithm is implemented. We present here the effects of some parameters of the system on its performances and on its recognition errors. This study shows a way to select the optimum values for some key parameters of the system to obtain minimum recognition error rates.
32 citations