Image Enhancement Processes for Myanmar Printed Text Recognition

Image enhancement processing step is the basiccrucial part of the OCR system as the recognitionaccuracy of OCR systems greatly depends on thequality of the input text image. Most of the OCRsystems, especially Myanmar OCR systems are littleeffort for preprocessing or image enhancementprocess. And Myanmar glyphs are complex in shape;combine with many circular, dots and line and pixeldistribution to form this scripts are not uniform.Therefore, making the input image to be better isimportant for our native OCR system. In this paperwe propose image enhancement processes suit that,through reducing noise, separating text andbackground, skew detection and rotation, improvesthe accuracy of the OCR System for Myanmarprinted text. These steps can produce a refined imagethat is ready for the segmentation or characterextraction process of the OCR system

Optical Character Recognition System For Myanmar Printed Documents

Automatic machine-printed OpticalCharacters or texts Recognizers (OCR) arehighly desirable for a multitude of modern ITapplications, including Digital Library software.However, the state of the art OCR systems can’tdo for Myanmar scripts as our language posemany challenges for document understanding.Therefore, we design an Optical CharacterRecognition System for Myanmar PrintedDocument (OCRMPD), with several proposetechniques that can automatically recognizeMyanmar printed text from document image. Inorder to get more accuracy system, we proposethe method for isolation of the character imageby using not only the projection methods but alsostructural analysis for wrongly segmentedcharacters. To reveal the effectiveness of oursegmentation technique, we follow a new hybridfeature extraction method and choose the SVMclassifier for recognition of the character image.The proposed algorithms have been tested on avariety of Myanmar printed documents and theresults of the experiments indicate that themethods can increase the segmentation accuracyas well as recognition rates

Speech Enhancement Techniques for Noisy Speech in Real World Environments

Communication between computer andhuman has become increasingly popular in todayworld. Investigation of human emotion importance isalso growing in several domains. But under realworld condition, speech signal is often, corruptedwith several noise types and the accuracy ofrecognition is degraded from these noisy signal.Therefore this paper focuses on the speechenhancement techniques to develop emotionrecognition system for the noisy signal in the realworld environment. The various popularenhancement techniques are analyzed by adding thebackground noise to the clean signal using variousSNR. To test the accuracy of the system, the widelyused MFCC signal features are against with the SVMclassifier. Results after enhancing were compared tothat noisy signal and that clean signal to measure thesystem performance. The experimental results showthe best performance algorithm and all enhancementalgorithms improve the emotion recognition systemperformance under various SNRs level of real worldbackground noise

Review of Peter Baumann's Epistemic Contextualism: A Defense, Oxford University Press, 2016

The helminth Strongyloides stercoralis, which is transmitted through soil, infects 30&plusmn;100 million&nbsp;people worldwide. S. stercoralis reproduces sexually outside the host as well as asexually&nbsp;within the host, which causes a life-long infection. To understand the population structure and&nbsp;transmission patterns of this parasite, we re-sequenced the genomes of 33 individual S. stercoralis nematodes collected in Myanmar (prevalent region) and Japan (non-prevalent region).&nbsp;We utilised a method combining whole genome amplification and next-generation sequencing&nbsp;techniques to detect 298,202 variant positions (0.6% of the genome) compared with the reference&nbsp;genome.</p

Genome-Wide Analyses of Individual <i>Strongyloides stercoralis</i> (Nematoda: Rhabditoidea) Provide Insights into Population Structure and Reproductive Life Cycles - Fig 4

<p>(A) Phylogenetic network analyses based on SNPs in the genome of <i>S</i>. <i>stercoralis</i>. (B) A maximum likelihood tree of SNPs in the mitochondrial genomes of <i>S</i>. <i>stercoralis</i> samples. The scale bars show the number of nucleotide substitutions per site. Branches marked with \\ indicate a two-fold shortening of branches (only for practical purposes).</p

Pair-wise genetic distances (π) of genomes between samples.

<p>Distances between samples from Japan and Myanmar (green dots) were generally higher than those of the same country. Inter-host comparisons (red dots) show higher πvalues than those of within-host comparisons (blue dots) in Myanmar. The differences are ambiguous in the Japanese samples.</p

<i>Strongyloides stercoralis</i> samples used in this study.

<p><i>Strongyloides stercoralis</i> samples used in this study.</p

Principal component analyses of variant data.

<p>(A) a plot including the reference genome strain (USA). Variances represented by PC1 = 40.1% and PC2 = 14.1%. (B) Japanese and Myanmar samples only. Variance represented by PC1 = 28.4% and PC2 = 10.3%. Totals of 234,398 and 128,040 variant positions were included in the PCA analysis for Fig 2A and 2B, respectively.</p