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A System for the Recognition of Handwritten Yorùbá Characters
 

A System for the Recognition of Handwritten Yorùbá Characters

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© Abdulrahman O. Ibraheem & Odetunji A. Odejobi

© Abdulrahman O. Ibraheem & Odetunji A. Odejobi

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    A System for the Recognition of Handwritten Yorùbá Characters A System for the Recognition of Handwritten Yorùbá Characters Presentation Transcript

    • A SYSTEM FOR THE RECOGNITITON OF HANDWRITTEN YORÙBÁ CHARACTERS BY ABDULRAHMAN O. IBRAHEEM AND ODETUNJI A. ODEJOBI AGIS 2011 Ethiopia Obafemi Awolowo University, Ile-Ife, Nigeria
    • In this presentation..
      • Provide an overview of the issues in Yoruba handwritten character recognition
      • Discuss the literature in character recognition
      • Discuss an approach based on Bayesian classifier and a decision tree to recognition of the character
      • Discuss our results
      • State on-going work
    • YORUBA ORTHOGRAPHY
      • Yorùbá orthography comprises roman letters
        • With diacritical marks to indicate tone
        • under dot for some phone distinction
      • We focus on Uppercase letter in this work
      • The uppercase letters of the Yoruba orthography are
        • À A Á B D È E É Ẹ̀ Ẹ Ẹ́ F G GB H Ì I Í J K L M N Ò O Ó ̀Ọ Ọ Ọ́ P R Ş T Ù U Ú W Y.
      AGIS 2011 Ethiopia
    • SCOPE
      • The Yoruba letters considered in this work are:
      • À Á È É Ẹ Ẹ̀ Ẹ́ Ì Í
      • Ò Ó Ọ ̀Ọ Ọ́ Ş Ù Ú
      • Notice that they all come from just six roman letters ( termed the six Bayesian classes ):
      • A E I O U S
      AGIS 2011 Ethiopia
    • REVIEW OF LITERATURE
      • Pattern recognition systems, including character recognition ones, include two main modules:
        • a feature extraction module
        • classification module (Duda et. al 2000).
      • This divides our literature review into two: a review of features, and a review of classifier techniques.
    • LITERATURE REVIEW OF FEATURES
      • Hu (1962) derived seven invariant geometric features (called Hu moment invariants) in terms of a number of normalized central moments.
      • But they contain redundant information and that are not invariant under affine transformations such as slants.
      • Teague (1980) addressed the problem of information redundancy by proposing the use of Zernike moments (ZM)
      • The problem of affine transformations was addressed by Flusser and Suk (1994) who derived a class of moments called Affine Moment Invariants (AMI) .
    • LITERATURE REVIEW OF FEATURES (CONTD)
      • AMIs were shown to be superior to Hu moments in experiments that involved the recognition of slanted characters. (Flusser and Suk; 1994)!
      • But AMIs are also not free from the information redundancy problem.
      • Although ZMs save on information redundancy, they are not very robust to noise.
      • Kan and Srinath (2002) compared ZMs with a class of moments known as Orthogonal Fourier-Mellin moments (OFMM).
    • LITERATURE REVIEW OF FEATURES (CONTD)
      • We concluded that OFMM leads to lower noise to signal ratio.
      • BUT, it is computationally too involved.
      • We adopted the Frey and Slate (1991) approach which involves less computations.
      • This approach has been used successfully, e.g Schwenk and Bengio (1997), Breuel (2003), and Husnain and Naweed (2009).
    • LITERATURE REVIEW OF FEATURES (CONTD)
      • This approach is to essentially augment low order geometric moments with intuitively defined features.
      • This is the direction we favor in this work.
    • THE PROPOSED SYSTEM
      • The system we have developed comprises six stages:
        • a pre-processing stage;
        • a segmentation stage;
        • a feature extraction stage;
        • a Bayesian classification stage;
        • a decision tree;
        • A stage for combining the results of the previous two stages into a single class label
    • ILLUSTRATION OF THE SYSTEM Output class label roman letter diacritical marks Pre-processing stage Input Yoruba character Segmentation stage Feature extraction stage Decision tree stage Results fusion stage Bayesian classification stage
    • DATA PREPARATION AND THE PRE-PROCESSING STAGE
      • The Bayesian Stage of our system is data-driven.
      • The Bayesian classifier serves to distinguish amongst the six Bayesian classes:
      • A E I O U S
      • For each of the six Bayesian classes, we collected 48 handwritten samples from volunteer writers.
    • DATA PREPARATION AND THE PRE-PROCESSING STAGE (CONTD)
      • Our volunteer writers were male and female students of the Obafemi Awolowo University, Ile-Ife, Osun state Nigeria whose ages lied essentially between sixteen and thirty five years.
      • Volunteers wrote on plain white A4 sheets.
      • Each volunteer provided a single sample of each of the seventeen characters
    • DATA PREPARATION AND THE PRE-PROCESSING STAGE (CONTD)
      • The A4 sheets were scanned, and with the aid of Paint graphics program, we cropped 40 samples of each of the Bayesian classes.
      • Of these, we used 40 samples per Bayesian class to train the Bayesian classifier.
      • We reserved eight samples from each of the seventeen classes for system evaluation.
    • SAMPLES CROPPED SAMPLES CROPPED SAMPLES DIACRITIC REMOVE
    • DATA PREPARATION AND THE PRE-PROCESSING STAGE (CONTD)
      • In the Matlab environment, we pre-processed each of the samples that were used to train the Bayesian classifier using the following simple steps:
        • conversion of color character image to binary form; removal of “salt and pepper” noise; computation of bounding box; and normalization of resulting image to fit a grid.
    • CHARACTER DATA PROCESSING Cropped character Character after Binarisation Character after Noise removal Character and its bounding
    • THE SEGMENTATION STAGE
      • The segmentation stage attempts to isolate the roman letter from the diacritical marks.
      • It is able to handle both connected-at-core characters and broken-at-core characters.
      • The roman letter of connected-at-core Yoruba characters do not contain breakages; the roman letter of broken-at-core Yoruba characters do.
    • A Connected-at-core Yoruba character. An under dot (tail) in the lower region of a Yoruba character. The lower region ( LR ) of a Yoruba character A grave mark in the upper Region of a Yoruba character. (Goes to the decision tree). Yoruba character. The upper region ( UR ) of a Yoruba character . The middle region ( MR )of a Yoruba character character. The roman letter of a Yoruba character.
    • A BROKEN-AT-CORE YORUBA CHARACTER A tail in the LR of a Yoruba character. The largest object in a Yoruba character. The character’s RCLO The character’s LR. ( This LR corresponds to the RBLO) The RBLO of a Yoruba character. ( This RBLO corresponds to the LR) A diacritical mark in the UR of a Yoruba character. A pseudo- diacritic in the RALO character. A breakage in the roman letter of a Yoruba character The RALO of a Yoruba character .( This RALO does not correspond to the UR) The UR of a Yoruba character .
    • SOME TREMINOLOGIES USED IN FIGURE
      • UR: Upper Region
      • MR: Middle Region
      • LR: Lower Region
      • RALO: Region Above Largest Object
      • RCLO: Region Containing Largest Object.
      • RBLO: Region Below Largest Object.
    • THE SEGMENTATION ALGORITHM
      • Identify the largest object in the image by finding the object with the maximum area.
      • 2) Do a tentative segmentation of the image into regions: the region above the largest object (RALO); the region containing the largest object (RCLO); and the region below the largest object (RBLO).
      • 3) If RALO contains two objects, release the lower one into RCLO.
      • 4) If RALO contains a single object, calculate the absolute value of
      • the object’s slope.
    • THE SEGMENTATION ALGORITHM (CONTD)
      • If the slope in step 4 above is less than 0.35,
      • release this single object into RCLO .
      • If RBLO contains two objects, release the upper one
      • into RCLO.
      • If RBLO contains a single object, calculate the
      • absolute value of the object’s slope.
      • If the slope in step 7 above is less than 2.0, release
      • this single object into RCLO .
    • THE DECISION TREE STAGE
      • The segmentation stage isolates the roman letter from the diacritical marks.
      • The decision tree takes the diacritical marks and tries to recognize them.
    • CHARACTER DECISION TREE GWT Grave With Tail; AWT Acute With Tail GDT Grave Devoid of Tail; ADT Acute Devoid of Tail; MWT Macron With Tail MDT for macron devoid of tail. Number of Elements up 0 GDT Slope up MDT Slope up 0 Number of elements down 1 Number of elements down GWT ADT MWT +ve -ve 0 1 -ve +ve AWT
    • TERMINOLOGIES IN THE DECISION TREE
      • GWT: Grave With Tail
      • AWT: Acute With Tail
      • GDT: Grave Devoid of Tail
      • ADT: Acute Devoid of Tail
      • MWT: Macron With Tail
      • MDT: Macron Devoid of tail
    • THE FEATURE EXTRACTION STAGE
      • The segmentation isolates the roman letter of the Yoruba character from the diacritical marks.
      • The roman letter goes to a Bayesian classifier via a feature extraction stage.
      • The feature extraction stage extracts eight geometric features.
    • THE EIGHT GEOMETRIC FEATURES WE EMPLOYED
      • Mean of character widths
      • Mean of character heights
      • Correlation of character heights with horizontal position.
      • Second order normalized central moment
    • THE EIGHT GEOMETRIC FEATURES WE EMPLOYED
      • Second order normalized central moment
      • Haralick’s circularity
      • Third order normalized moment
      • Third order normalized moment
    • A DISPLAY OF THE FEATURES AND THEIR FORMULAE FEATURE NO. FEATURE AND EXPLANATION OF FORMULA FORMULA 1 Mean character width. stands for width at row . is the height of the character. 2 Mean character height. . stands for height at column . is the width of the character. 3 Correlation of height with horizontal distance. 4 Second order normalized central moment . is the raster coordinate of the row of character centroid. 5 Second order normalized central moment. is the raster coordinate of the column of character centroid. 6 Haralick’s circularity. is the distance from every point on the character to the centroid of character. is the mean of the s; is their standard deviation. 7 Third order normalized moment 8 Third order normalized moment
    • BAYESIAN DECISION THEORY (BDT) AND THE BAYESIAN CLASSIFIER STAGE.
      • Bayesian Decision Theory is a fundamental tool in pattern recognition.
      • It has been applied with success in the character recognition domain (Husnain and Naweed; 2009).
      • Roughly speaking, a Bayesian classifier puts an object in the class it has the highest probability of belonging to.
    • BDT AND THE BAYESIAN CLASSIFIER STAGE (CONTD).
      • Here is Baye’s formula :
      • c is the number of Bayesian classes (six)
      • is the posterior probability
    • BDT AND THE BAYESIAN CLASSIFIER STAGE (CONTD).
      • is the class-conditional probability. We take it to be a Gaussian for handwritings (Duda et. al 2000).
      • is the prior probability
      • is called the evidence: it is the same across all classes.
    • BDT AND THE BAYESIAN CLASSIFIER STAGE (CONTD).
      • We can view Baye’s formula as a decision rule formula with a zero-one cost function (Duda et. al 2000).
      • Using the facts that decision rules are not affected by: addition of constants; multiplication by positive constants; and the effect of strictly increasing functions, such as the natural logarithm function, we can ultimately obtain a decision rule formula equivalent to Baye’s rule (Duda et. al 2000):
    • BDT AND THE BAYESIAN CLASSIFIER STAGE (CONTD).
      • The above equation is the form we employed in our Bayesian classifier.
      • is the extracted feature; it is 8-dimensional.
      • is the covariance of the i-th class,
      • being its determinant and its inverse.
    • BDT AND THE BAYESIAN CLASSIFIER STAGE (CONTD).
      • is the mean feature vector of the i-th class.
      • The rule is to compute for all from 1 to and pick the class corresponding to the maximum.
      • The obtained result is fused with the result of the decision tree in a result fusion stage.
    • THE RESULT FUSION STAGE
      • Result fusion is handled in our system by the following equation:
      • is the final class label produced by the system. It can take integer values between 1 and 17
    • THE RESULT FUSION STAGE (CONTD)
      • is the label from the Bayesian classifier. This is an integer value between 1 and 6.
      • tracks tonal marks. = 0 indicates macrons, = 1 indicates grave marks.
      • = 2 indicates acute marks.
      • tracks under dots = 0 indicates absence of under dot = 3 indicates
      • presence of under dot.
    • THE RESULT FUSION STAGE (CONTD)
      • is a function of that “pads the equation up” to achieve the neat one-to-one correspondence:
      characters À Á È É Ẹ Ẹ̀ Ẹ́ Ì Í Ò Ó Ọ ̀Ọ Ọ́ Ş Ù Ú final_label 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
    • THE RESULT FUSION STAGE (CONTD)
      • is defined in the table below:
      bdt_label f(bdt_label) 1 -1 2 0 3 4 4 5 5 9 6 9
    • RESULTS
      • We tested the system in two folds.
      • In the first fold, with eight independent samples from each of the seventeen classes.
      • A recognition rate of 91.18% was obtained.
    • RESULTS (CONTD)
      • In the second fold, we tested the system with 3 non-independent samples from each of the six Bayesian classes.
      • A recognition rate of 94.44% was obtained.
    • CONCLUSION AND FUTURE WORK
      • In summary, we have developed a technique for the classification of diacritically marked uppercase Yorùbá handwritten charaters in offline mode.
      • Our system involves six stages:
        • Pre-processing
        • Segmentation
        • Decision tree
        • Feature extraction
        • Bayesian classification
        • Result fusion
    • Conclusion cont…
      • We built the Bayesian stage using forty samples per Bayesian class.
      • We tested the system on independent and non-independent samples.
      • On independent samples, a recognition rate of 91.18% was obtained.
      • On non-independent samples, a recognition rate of 94.44 % was recorded.
    • ONGOING WORK
      • We plan to extend this work in two directions:
        • We wish to extend the system to cover the entire Yorùbá orthography, including the lower case letters and cursive scripts.
        • Deploy the system in Table environment
      AGIS 2011 Ethiopia