Character recognition — A review

Govindan, V. K.; Shivaprasad, A. P.

doi:10.1016/0031-3203(90)90091-x

Cited by 394 publications

(117 citation statements)

References 59 publications

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“…-Character recognition: the recognition procedure consists of two steps: feature extraction and classification. Govindan [22] classifies the features into two categories, namely, structural and statistical, while Bunke [23] estimates that the structural approach is closer to the human way of recognition. Following this approach in our system, a 280-dimension vector is created for each character, consisting of histograms and profiles.…”

Section: System Presentationmentioning

confidence: 99%

An unconstrained handwriting recognition system

Kavallieratou¹,

Fakotakis²,

Kokkinakis

2002

International Journal on Document Analysis and Recognition

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Abstract. In this paper, an integrated offline recognition system for unconstrained handwriting is presented. The proposed system consists of seven main modules: skew angle estimation and correction, printedhandwritten text discrimination, line segmentation, slant removing, word segmentation, and character segmentation and recognition, stemming from the implementation of already existing algorithms as well as novel algorithms. This system has been tested on the NIST, IAM-DB, and GRUHD databases and has achieved accuracy that varies from 65.6% to 100% depending on the database and the experiment.

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Section: System Presentationmentioning

confidence: 99%

An unconstrained handwriting recognition system

Kavallieratou¹,

Fakotakis²,

Kokkinakis

2002

International Journal on Document Analysis and Recognition

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“…It contributes immensely to the sequence of a computerization technique and could enhance the interface among human and machine in so many purposes. Various study attempts have been centre of attention on innovative methods and schemes that would decrease the processing moment while offering maximum recognition precision [4].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Handwritten Character Recognition (HCR) Techniques for English Alphabets

Sonkusare¹,

Sahu²

2016

AVC

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“…It contributes immensely to the advancement of an automation process and can improve the interface between man and machine in numerous applications. Several research works have been focusing toward evolving newer techniques and methods that would reduce the processing time while providing higher recognition accuracy [1] [2].…”

Section: Introductionmentioning

confidence: 99%

Diagonal Feature Extraction Based Handwritten Character System Using Neural Network

Pradeep¹,

Srinivasan²,

Himavathi³

2010

IJCA

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A handwritten character recognition system using multilayer Feed forward neural network is proposed in this paper. The character data set suitable for recognizing postal addresses contains 38 elements which include 26 alphabets, 10 numerals and 2 symbols. Fifteen different handwritten data sets were used for training the neural network for classification and recognition of the characters. Three different orientations, namely, horizontal, vertical and diagonal directions are used for extracting 54 features from each character. The trained neural recognition system is tested for various inputs and found to perform well. The diagonal orientation for feature extraction is identified to be the most suitable method as it yields higher recognition accuracy. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form.

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Character recognition — A review

Cited by 394 publications

References 59 publications

An unconstrained handwriting recognition system

An unconstrained handwriting recognition system

A Survey on Handwritten Character Recognition (HCR) Techniques for English Alphabets

Diagonal Feature Extraction Based Handwritten Character System Using Neural Network

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