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Intelligent character recognition

About: Intelligent character recognition is a(n) research topic. Over the lifetime, 4539 publication(s) have been published within this topic receiving 158096 citation(s).

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Papers
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Journal ArticleDOI: 10.1109/5.726791
Yann LeCun1, Léon Bottou2, Léon Bottou3, Yoshua Bengio4  +3 moreInstitutions (5)
01 Jan 1998-
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

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Topics: Neocognitron (64%), Intelligent character recognition (64%), Artificial neural network (60%) ...read more

34,930 Citations


Journal ArticleDOI: 10.1198/TECH.2007.S518
01 Aug 2007-Technometrics
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.

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16,640 Citations


Journal ArticleDOI: 10.1109/34.824819
Abstract: The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

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6,278 Citations


Open accessBook
Brian D. Ripley1, N. L. HjortInstitutions (1)
01 Jan 1996-
Abstract: From the Publisher: Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.

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5,508 Citations


Journal ArticleDOI: 10.1109/34.824821
Réjean Plamondon1, Sargur N. Srihari2Institutions (2)
Abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Topics: Intelligent character recognition (68%), Handwriting (63%), Handwriting recognition (62%) ...read more

2,535 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20211
20202
20195
201812
2017100
2016226

Top Attributes

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Topic's top 5 most impactful authors

Horst Bunke

49 papers, 3.7K citations

Venu Govindaraju

33 papers, 790 citations

Masaki Nakagawa

30 papers, 645 citations

Ching Y. Suen

29 papers, 2.1K citations

Sargur N. Srihari

27 papers, 3.3K citations

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