Journal ArticleDOI
Online and off-line handwriting recognition: a comprehensive survey
TLDR
The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.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.read more
Citations
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Reading Digits in Natural Images with Unsupervised Feature Learning
TL;DR: A new benchmark dataset for research use is introduced containing over 600,000 labeled digits cropped from Street View images, and variants of two recently proposed unsupervised feature learning methods are employed, finding that they are convincingly superior on benchmarks.
Journal ArticleDOI
A Novel Connectionist System for Unconstrained Handwriting Recognition
TL;DR: This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies, significantly outperforming a state-of-the-art HMM-based system.
Proceedings ArticleDOI
Adversarial machine learning
TL;DR: In this article, the authors discuss an emerging field of study: adversarial machine learning (AML), the study of effective machine learning techniques against an adversarial opponent, and give a taxonomy for classifying attacks against online machine learning algorithms.
Proceedings ArticleDOI
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
TL;DR: This work presents a "$1 recognizer" that is easy, cheap, and usable almost anywhere in about 100 lines of code, and discusses the effect that the number of templates or training examples has on recognition, the score falloff along recognizers' N-best lists, and results for individual gestures.
Book ChapterDOI
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks
Alex Graves,Jürgen Schmidhuber +1 more
TL;DR: This paper introduces a globally trained offline handwriting recogniser that takes raw pixel data as input and does not require any alphabet specific preprocessing, and can therefore be used unchanged for any language.
References
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TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
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The self-organizing map
TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
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Automatic signature verification and writer identification — the state of the art
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