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.
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25 Mar 2015
TL;DR: This work provides a comprehensive review of these methods for off-line handwritten Arabic text recognition and presents recognition rates and descriptions of the databases used for the discussed approaches.
Abstract: Research in Arabic handwritten recognition has been of growing interest in the last few decades. This is mainly due to its broad spectrum of applications in different fields such as bank check processing, form data entry, postal mail sorting, automatic processing of old manuscripts, etc. In the literature, numerous techniques have been proposed for feature extraction and applied to various types of images. This work provides a comprehensive review of these methods for off-line handwritten Arabic text recognition. It also presents recognition rates and descriptions of the databases used for the discussed approaches. This paper includes background on the field, discussion of feature extraction methods, and future research directions.
20 citations
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26 Oct 2004TL;DR: Different from conventional approach, proposed approach can avoid the character segmentation error successfully and Experimental results show the proposed approach is very effective.
Abstract: A handwritten Chinese address recognition (HCAR) system is proposed in this paper. Handwritten Chinese address recognition is a difficult problem. Handwritten Chinese characters are characterized by large vocabulary, complicate structure, irregular distortion and touching characters etc. The proposed approach takes good advantage of Chinese address knowledge, and applies key character extraction and holistic word matching to solving the problem. Different from conventional approach, proposed approach can avoid the character segmentation error successfully. Experimental results show the proposed approach is very effective.
20 citations
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04 May 1998TL;DR: A technique is presented that segments difficult printed and cursive handwriting, and then classifies the segmented characters and then identifies the characters which remain following the segmentation process.
Abstract: Artificial neural networks (ANNs) have been successfully applied to optical character recognition (OCR) yielding excellent results. In this paper a technique is presented that segments difficult printed and cursive handwriting, and then classifies the segmented characters. A conventional algorithm is used for the initial segmentation of the words, while an ANN is used to verify whether an accurate segmentation point has been found. After all segmentation points have been detected another ANN is used to identify the characters which remain following the segmentation process. The C programming language, the SP2 supercomputer and a SUN workstation were used for the experiments. The technique has been tested on real-world handwriting scanned from various staff at Griffith University, Gold Coast. Some preliminary experimental results are presented in this paper.
20 citations
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07 Mar 1996TL;DR: A word-level recognition system for machine-printed Arabic text has been implemented and has obtained promising word recognition rates on low-quality multifont text imagery.
Abstract: Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream. Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.
20 citations