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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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Journal ArticleDOI
TL;DR: This work describes the design and implementation of an Arabic word recognition system that recognizes the input word by detecting a set of “shape primitives” on the word and tries to maximize the a posteriori probability of the arrangement of symbol models.
Abstract: As the recognition rates and speeds of optical character recognition (OCR) systems steadily improve, the problem of OCR--and subsequently research interest--is shifting from recognizing: isolated, high-quality characters to reading cursive scripts and degraded documents. In recognizing such texts, a major undertaking is segmenting cursive words into characters and isolating merged characters. In OCR systems that recognize cursive text, the segmentation subsystem becomes the pivotal stage in the system to which a sizable portion of processing is devoted and a considerable share of recognition errors is attributed. The most notable feature of Arabic writing is its cursiveness. It also poses the most difficult problem for recognition algorithms. In this work, we describe the design and implementation of a system that is automatically trainable and that recognizes noisy and cursive words. To recognize a word, the system does not segment it into symbols (character shapes) in advance; rather, it recognizes the input word by detecting a set of "shape primitives" on the word. It then matches the regions of the word (represented by the detected primitives) to a set of symbol models. A spatial arrangement of symbol models that are matched to regions of the word, then, becomes the description of the recognized word. Since the number of potential arrangements of all symbol models is large, the system imposes a set of word structure and spatial consistency. It searches the space comprised of the arrangements that satisfy the constraints and tries to maximize the a posteriori probability of the symbol-models' arrangement. Large-scale experimentation with the system on isolated characters reveals that it has a recognition rate of 99.7% for synthetically degraded symbols and 94.1% for scanned symbols. Experimentation on isolated words reveals that the system has a recognition rate of 99.4% for noise-free words, 95.6% for synthetically degraded words, and 73% for scanned words. The main theoretical contribution of this work is in laying the foundation for a segmentation-free approach for Arabic word recognition. Recognition is based on maximizing the probability of the word given the detected primitives. The system is designed to minimize training effort and is extensible as training determines the symbols the system recognizes.

49 citations

Proceedings ArticleDOI
01 Jan 1992
TL;DR: The paper is a survey of techniques for segmenting images of handwritten text into individual characters and several approaches to each are outlined, and each is analyzed for its relevance to printed, cursive, on-line and off-line input data.
Abstract: The paper is a survey of techniques for segmenting images of handwritten text into individual characters. The topic is broken into two categories: segmentation and segmentation-recognition techniques. Several approaches to each are outlined, and each is analyzed for its relevance to printed, cursive, on-line and off-line input data. >

49 citations

Proceedings ArticleDOI
09 Oct 1994
TL;DR: A complete OCR system is described for documents of single Bangla (Bengali) font by a combination of template and feature matching approach and has an accuracy of about 96%.
Abstract: In this paper a complete OCR system is described for documents of single Bangla (Bengali) font. The character shapes are recognized by a combination of template and feature matching approach. Images digitized by flatbed scanner are subjected to skew correction, line, word and character segmentation, simple and compound character separation, feature extraction and finally character recognition. A feature based tree classifier is used for simple character recognition. Preprocessing like thinning and skeletonization is not necessary in our scheme and hence the system is quite fast. At present, the system has an accuracy of about 96%. Also, some character occurrence statistics have been computed to model an error detection and correction technique in the near future.

49 citations

Journal ArticleDOI
TL;DR: A ''critical region analysis'' technique which highlights the critical regions that distinguish one character from another similar character is proposed and a record high recognition rate of 99.53% on the ETL-9B database is obtained.

49 citations

Patent
05 Dec 2004
TL;DR: In this paper, the combination of speech recognition with handwriting and/or character recognition was proposed, and the best-scoring recognition candidates were selected as a function of recognition of both handwritten and spoken representations of a sequence of one or more words to be recognized.
Abstract: The invention relates to the combination of speech recognition with handwriting and/or character recognition. This includes the innovation of selecting one or more best-scoring recognition candidates as a function of recognition of both handwritten and spoken representations of a sequence of one or more words to be recognized. It also includes the innovation of using character or handwriting recognition of one or more letters to alphabetically filter speech recognition of one or more words. It also includes the innovations of using speech recognition of one or more letter-identifying words to alphabetically filter handwriting recognition, and of using speech recognition to correct handwriting recognition of one or more words.

49 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202314
202241
20201
20192
20189
201751