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Journal ArticleDOI

Recognition and verification of unconstrained handwritten words

TLDR
A novel approach for the verification of the word hypotheses generated by a large vocabulary, offline handwritten word recognition system that has improved the word recognition rate as well as the reliability of the recognition system, while not causing significant delays in the recognition process.
Abstract
This paper presents a novel approach for the verification of the word hypotheses generated by a large vocabulary, offline handwritten word recognition system. Given a word image, the recognition system produces a ranked list of the N-best recognition hypotheses consisting of text transcripts, segmentation boundaries of the word hypotheses into characters, and recognition scores. The verification consists of an estimation of the probability of each segment representing a known class of character. Then, character probabilities are combined to produce word confidence scores which are further integrated with the recognition scores produced by the recognition system. The N-best recognition hypothesis list is reranked based on such composite scores. In the end, rejection rules are invoked to either accept the best recognition hypothesis of such a list or to reject the input word image. The use of the verification approach has improved the word recognition rate as well as the reliability of the recognition system, while not causing significant delays in the recognition process. Our approach is described in detail and the experimental results on a large database of unconstrained handwritten words extracted from postal envelopes are presented.

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Citations
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Journal ArticleDOI

Off-line recognition of realistic Chinese handwriting using segmentation-free strategy

TL;DR: This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle off-line recognition of realistic Chinese handwriting, where character segmentation stage is avoided prior to recognition.
Journal ArticleDOI

HMM-based Indic handwritten word recognition using zone segmentation

TL;DR: An efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and then recognize the corresponding zones to reduce the number of distinct component classes compared to the total number of classes in Indic scripts is proposed.
Journal ArticleDOI

On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm

TL;DR: A handwriting recognition system based on visual coding and genetic algorithm ''GA'' applied on Arabic script and the results obtained prove that the new method based on hybridization between visual codes and GA is a powerful method.
Journal ArticleDOI

Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

TL;DR: It is shown experimentally that explicit state duration modeling in the SCHMM framework can significantly improve the discriminating capacity of the SCHMMs to deal with very difficult pattern recognition tasks such as unconstrained handwritten Arabic recognition.
Proceedings ArticleDOI

On-Line Handwritten Character Recognition with 3D Accelerometer

TL;DR: A pen-style hardware and analysis software for the recognition of handwritten characters and the database clearly demonstrates the usefulness of the acceleration-based handwritten character recognition system without touching screen or pad.
References
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Journal ArticleDOI

Online and off-line handwriting recognition: a comprehensive survey

TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
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An overview of character recognition focused on off-line handwriting

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Journal ArticleDOI

Off-line cursive script word recognition

TL;DR: In this paper, a word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words, and a unique feature representation is generated bottom-up from the image using statistical dependences between letters and features.
Book

Pattern Classification: A Unified View of Statistical and Neural Approaches

TL;DR: In this article, a classification based on statistical models determined by First-and Second Order Statistical Moments is proposed, which is based on Mean-Square Functional Approximations (MFFA).
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