Download - Khmer ocr using gfd_seminar_day
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Sovann EN
5th Year Engineering student
Dept. Computer Science & Communication
Institute of Technology of Cambodia
Phnom Penh, Cambodia
Khmer OCR System
1Khmer OCR System
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Native of research work
• A collaboration work with Mr. Kruy Vanna, PhD A collaboration work with Mr. Kruy Vanna, PhD
student at kameyama Laboratory , GITS, Waseda student at kameyama Laboratory , GITS, Waseda
UniversityUniversity
• The Objective is to produce a reliable Khmer OCR The Objective is to produce a reliable Khmer OCR
system which is independent of Font and Sizesystem which is independent of Font and Size
2Khmer OCR System
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Outline
• Overview of OCROverview of OCR
• Training dataTraining data
• Pre-processing and Segmentation Pre-processing and Segmentation
• features extraction and recognition processfeatures extraction and recognition process
• Post-processingPost-processing
3Khmer OCR System
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Outline
4Khmer OCR System
• Overview of OCROverview of OCR
• Training dataTraining data
• Pre-processing and features extractionPre-processing and features extraction
• Training process and recognition systemTraining process and recognition system
• Post-processingPost-processing
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What is OCR ???
• Optical Character Recognition (OCR) is the Optical Character Recognition (OCR) is the mechanical or electronic translation of scanned mechanical or electronic translation of scanned images of handwritten, typewritten or printed text images of handwritten, typewritten or printed text into machine-encoded textinto machine-encoded texthttp://en.wikipedia.org/wiki/Optical_character_recognitionhttp://en.wikipedia.org/wiki/Optical_character_recognition
5Khmer OCR System
• a document , a scan process , ocr system and an a document , a scan process , ocr system and an out put text. (put all these pictures here. Png out put text. (put all these pictures here. Png & .docx)& .docx)
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Its applications…
Lister certaines applicationsLister certaines applications
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Overview of OCR System
Khmer OCR System7
Training data
Recognition system
(Knowledge)
Pre-processing
Features extraction
Input pattern
Character Reordering
Recognition result
Training processRecognition process Features
selection/reduction
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Outline
• Overview of OCROverview of OCR
• Training dataTraining data
• Pre-processing and features extractionPre-processing and features extraction
• Training process and recognition systemTraining process and recognition system
• Post-processingPost-processing
8Khmer OCR System
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Training Data
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• To cover more font and size, it is necessary to To cover more font and size, it is necessary to
have more training sample of different font have more training sample of different font
Train Computer to recognize each of them is Train Computer to recognize each of them is ១១
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Outline
• Overview of OCROverview of OCR
• Training dataTraining data
• Pre-processing and features extractionPre-processing and features extraction
• Training process and recognition systemTraining process and recognition system
• Post-processingPost-processing
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Pre-processing and Segmentation
• Pre-processing aims to produce data that are Pre-processing aims to produce data that are
easy for the OCR systems to operate accuratelyeasy for the OCR systems to operate accurately
• The main objectives of pre-processing are :The main objectives of pre-processing are :• Binarization Binarization • Particle removalParticle removal
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Binarization (Thresholding)
• Image linearization (thresholding) refers to the conversion of a gray-scale image into a binary image.
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Particle removal
• Salt-and-pepper noise is a kind of noise which is Salt-and-pepper noise is a kind of noise which is
usually caused by small unnecessary dots usually caused by small unnecessary dots
produced by either the scanner or the source produced by either the scanner or the source
document itself.document itself.
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Segmentation
• Segmentation aims to produce each component Segmentation aims to produce each component
to be recognized by the system. to be recognized by the system.
• The process is to separate the text of a page The process is to separate the text of a page
into each separate line, then to separate each into each separate line, then to separate each
line into Vertical Component, and finally produce line into Vertical Component, and finally produce
each independent symbol. each independent symbol.
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Segmentation
• Segmentation aims to produce each component Segmentation aims to produce each component
to be recognized by the system. to be recognized by the system.
• The process is to separate the text of a page The process is to separate the text of a page
into each separate line, then to separate each into each separate line, then to separate each
line into Vertical Component, and finally produce line into Vertical Component, and finally produce
each independent symbol. each independent symbol.
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Example using CCA
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Feature Extraction & Recognition
• In feature extraction stage, each character is represented as a feature vector which becomes its identity.
• The major goal of feature extraction is to extract a set of features which maximizes the recognition rate with the least amount of elements.
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Recognition Process
• GFD is derived by applying two-dimensional GFD is derived by applying two-dimensional
Fourier transform on a polar-raster sampled Fourier transform on a polar-raster sampled
shape image.shape image.
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Generic Fourier Descriptor
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well…this is well…this is កាកា
GFD Feature vectorGFD Feature vector
well…this is well…this is កាកា
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Recognition Process
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???
Input imageInput image Training ImageTraining Image
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Recognition Process
• The similarity between two shapes is measured The similarity between two shapes is measured
by the City-Block distance of the two feature by the City-Block distance of the two feature
vectors of the shape.vectors of the shape.
• The lower value means the more similar the The lower value means the more similar the
shapes are.shapes are.
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Post-processing : Reordering
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SegmentationSegmentation
Recognized wordRecognized word
ReorderingReordering
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Experimental Result
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precision
Recall
F-Mesure
• The test was conducted on a document with a The test was conducted on a document with a
resolution of 300 dpi of … symbols.resolution of 300 dpi of … symbols.
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Khmer OCR Using Generic Fourier Descriptor Back
Thank for your attention !!!
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Reference28
Khmer OCR Using Generic Fourier Descriptor Back
[1] V. Kruy. Preliminary Experiment on Khmer OCR. Kameyama Laboratory, Waseda Univerisy, Japan.
[2] Thesis for master degree, Khmer OCR, Vanna Kruy.
[3] D. Zhang and G. Lu. Shape-based image retrieval using generic Fourier descriptor. Gippsland School of Computing and InformationTechnology. Monash University. Churchill, Victoria 3842, Australia.
[4] Thesis for Doctoral Degree, chapter 6: Generic Fourier Descriptor, Dengsheng Zhang.
[5] J.C.Rupe. Vision-Based Hand Shape Identification for Sign Language Recognition. Department of Computer Engineering Kate Gleason College of Engineering Rochester Institute of Technology Rochester, NY.
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Reference29
Khmer OCR Using Generic Fourier Descriptor Back
[6] D. Dimov. A polar-Fourier-Wavelet’s Transform for Effective CBIR. 3rd ADBIS workshop on Data mining & Knowledge Discovery
[7] I. Lengieng, K. Sochenda and C. Sokhour. , Khmer OCR for Limon R1 Size 22 Report, PAN Localization Cambodia (PLC) of IDRC.er OCR
[8] A. Averbuch, R.R. Coifmany , D.L. Donohoz M. Eladx M. Israeli. Fast and Accurate Polar Fourier Transform. Department of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. Department of mathematics, Yale University, New Haven CT 06520-8283 USADepartment of Statistics, Stanford University, Stanford 94305-9025 CA. USA.
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