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Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.


Papers
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Journal ArticleDOI
01 Jul 1992
TL;DR: Intense research performed over the past 15 years to answer the most pressing recognition problems is described and the man-machine interfaces made possible by online handwriting recognition and anticipated advances in both hardware and software are discussed.
Abstract: For large-alphabet languages, like Japanese, handwriting input using an online recognition technique is essential for input accuracy and speed. However, there are serious problems that prevent high recognition accuracy of unconstrained handwriting. First, the thousands of ideographic Japanese characters of Chinese origin (called Kanji) can be written with wide variations in the number and order of strokes and significant shape distortions. Also, writing box-free recognition of characters is required to create a better man-machine interface. Intense research performed over the past 15 years to answer the most pressing recognition problems is described. Prototype systems are also described. The man-machine interfaces made possible by online handwriting recognition and anticipated advances in both hardware and software are discussed. >

109 citations

Journal ArticleDOI
TL;DR: An algorithm for text recognition/correction that effectively merges a bottom-up refinement process that is based on the utilization of transitional probabilities and letter confusion probabilities, known as the Viterbi algorithm [VA], together with a top-down process based on searching a trie structure representation of a lexicon.
Abstract: The capabilities of present commercial machines for producing correct text by recognizing words in print, handwriting and speech are very limited. For example, most optical character recognition [OCR] machines are limited to a few fonts of machine print, or text that is handprinted under certain constraints; any deviation from these constraints will produce highly garbled text. This paper describes an algorithm for text recognition/correction that effectively merges a bottom-up refinement process that is based on the utilization of transitional probabilities and letter confusion probabilities, known as the Viterbi algorithm [VA], together with a top-down process based on searching a trie structure representation of a lexicon. The algorithm is applicable to text containing an arbitrary number of character substitution errors such as that produced by OCR machines.

109 citations

Patent
Dar-Shyang Lee1, Lee-Feng Chien1, Aries Hsieh1, Pin Ting1, Kin Wong1 
06 Oct 2010
TL;DR: In this paper, a text region is identified in a video frame approximate to the on-screen guideline and cropped from the video frame, which is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and generates text in an editable symbolic form.
Abstract: A live video stream captured by an on-device camera is displayed on a screen with an overlaid guideline. Video frames of the live video stream are analyzed for a video frame with acceptable quality. A text region is identified in the video frame approximate to the on-screen guideline and cropped from the video frame. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and generates text in an editable symbolic form (the OCR'ed text). A confidence score is determined for the OCR'ed text and compared with a threshold value. If the confidence score exceeds the threshold value, the OCR'ed text is outputted.

109 citations

Proceedings ArticleDOI
05 Jun 2000
TL;DR: A new text detection and segmentation algorithm that is especially designed for being applied to color images with complicated background and to binarize efficiently the detected text areas so that they can be processed by standard OCR software.
Abstract: Text is a very powerful index in content-based image and video indexing. We propose a new text detection and segmentation algorithm that is especially designed for being applied to color images with complicated background. Our goal is to minimize the number of false alarms and to binarize efficiently the detected text areas so that they can be processed by standard OCR software. First, potential areas of text are detected by enhancement and clustering processes, considering most of constraints related to the texture of words. Then, classification and binarization of potential text areas are achieved in a single scheme performing color quantization and characters periodicity analysis. We report a high rate of good detection results with very few false alarms and reliable text binarization.

109 citations

Book
01 Jan 1998
TL;DR: Alpaydin and Gurgen, Comparison of Statistical and Neural Classifiers and their Applications to Optical Character Recognition and Speech Classification and Chen and Chang, Learning Algorithms and Applications of Principal Component Analysis.
Abstract: Lampinen, Pattern Recognition. Alpaydin and Gurgen, Comparison of Statistical and Neural Classifiers and their Applications to Optical Character Recognition and Speech Classification. Sun and Nekovei, MedicalImaging. Takeda and Omatu, Paper Currency Recognition. Cordella and Stefano, Neural Network Classification Reliability: Problems and Applications. Yagi, Kobayaski, and Matsumoto, Parallel Analog Image Processing: Solving Regularization Problems with Architecture Inspired by the Vertebrate Retinal Circuit. Setiono, Algorithmic Techniques and their Applications. Chen and Chang, Learning Algorithms and Applications of Principal Component Analysis. Merat and Villalobos, Learning Evaluation and Pruning Techniques.

108 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023186
2022425
2021333
2020448
2019430
2018357