handwritten oriya writer identification using zone segmentation technique

8/10/2019 handwritten oriya writer identification using zone segmentation technique

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Writer Identification Of Handwritten Oriya Script

Barid Baran Nayak Partha Pratim Roy Umapada Pal

Dept.of ECE Dept. of CS CVPR Unit

NIT Rourkela,India IIT Roorkee,India ISI Kolkata,India

Abstract

In handwriten writer identification and character recognition we have

done a image based analysis,where a scanned digital image containing

handwriten script is taken as input, then system translate it into an

machine editable readable digital text format. oriya language present

great challenges due to the large number of letters in alphabet set,the

sophisticated ways in which they combine and many letters areroundish and similar to look .

In this project an attempt is made to recognize the writers by use of

HISTOGRAM OF GRADIENT features of character image. The features so

obtained are passed through the HMM code which gives out the

identification result.

Keywords: character recognition.writer identification,histogram of

gradient,Hidden Markov Model(HMM)


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INTRODUCTION:-

Oriya is one of the many official languages in India; it is the official

language of Odisha and the second official language of Jharkhand.

Since it is an old language there are various old documents present

whose writers are unknown. My project deals with this problem. Its

main aim is to identify who is the writer. And the Other part of the

project is to identify each character written.

Due to the presence of complex features such as headline, vowels,

modifiers, etc., character segmentation in Oriya script is not easy. Also,

the position of vowels and compound characters make the

segmentation task of words into characters very complex. To take care

of this problem we tried a novel method considering a zone wise break

up of words and next HMM based recognition. In particular, the word

image is segmented into 3 zones, upper, middle and lower,

respectively. The components in middle zone are modelled using HMM.

By this zone segmentation approach we reduce the number of distinctcomponent classes compared to total number of classes in Oriya

character set. Once the middle zone portion is recognized, HMM based

forced alignment is applied in this zone to mark the boundaries of

individual components. The segmentation paths are extended later to

other zones Next, the residue components, if any, in upper and lower

zones in their respective boundary are combined to achieve the final

word level recognition.

Earlier template based approachwas followed for recognition purpose.

In this approach an unknown pattern was superimposed on the ideal

template is done, and then the degree of correlation between the two


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was used for the classification. But this approach became ineffective

because of noises and changes in hand writing. Hence now a days

feature based approachis used.

DIAGRAMS

Figure1: original oriya script


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Line segmentation:

Word segmentation:


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Zone segmentation

Figure 4: (a) Original Word. (b) Zone segmented word

(upper,mid,lower).

Figure 5: character segmentation from words.


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Results:

Conclusion:

The writer identification of writer was successfully carried out andsignificant results were obtained.A scheme for segmentation of

unconstrained Oriya handwrittentext into lines, words and characters is

proposed in this paper. Here, at first, the text image is segmented into

lines, and then lines are segmented into individual words. Next, for

character segmentation from words, initially, isolated and connected

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zone segmentation non zone

segmentation

Series 1

Series 1


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(touching) characters in a word are detected. Using structural,

topological and water reservoir concept-based features, touching

characters of the word are then segmented into isolated characters. To

the best of our knowledge,

this is the first work of its kind on Oriya text. The proposed waterreservoir-based approach can also be used for other Indian scripts where

touching patterns show similar behavior.

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handwritten oriya writer identification using zone segmentation technique

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