algorithmics and modular computations, Theory of Codes and Cryptography (3).From an analytical 1. RE Blahut. Theory and practice of error control codes. eecs.uottawa.ca/∼yongacog/courses/coding/ (3) R.E. Blahut,Theory and Practice of Error Control Codes, Addison Wesley, 1983. QA 268. Cached. Download as a PDF 457, Theory and Practice of Error Control CodesBlahut 1984 (Show Context). Citation Context..ontinued fractions.

Theory and practice of error control codes

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c-means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2%.

/pdf/lung-infection-segmentation-for-covid-19-pneumonia-based-on-kvw7ka7s69.pdf

Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images.

Automatic text detection and recognition (end-to-end text recognition) in real-life images are the main elements of many applications including blind and low vision assistance systems and self-driving cars. However, it is challenging to detect curved and vertical texts due to their color bleeding, font size variation, and complicated background. In this paper, a convolutional neural network-based pipeline is introduced to obtain high-level visual features and improve text detection and recognition efficiency. A pre-trained ResNet-50 network on ImageNet and SynthText for extracting low-level visual features was used in this study. Moreover, new improved ReLU layer (new.i.ReLU) blocks are used with a varied receptive field with a strong ability to detect text components even on curved surfaces in the proposed structure. A new improved inception layer (new.i.inception layers) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer. Also, we have proposed a pipeline framework for character recognition that is robust to irregular (curve and vertical) text. First, we introduced a novel algorithm for encoding pixel’s value to a new one called local word directional pattern (LWDP) that highlights the texture of the characters. Then, the output of LWDP was presented as an input image in the text recognition process. The experiments on standard benchmarks, including ICDAR 2013, ICDAR 2015, and ICDAR 2019 datasets, illustrated the superiority of the proposed architecture over prior works.

A novel pipeline framework for multi oriented scene text image detection and recognition

Cervical biopsy (biopsy) is an important part of the diagnosis of cervical cancer. The artificial classification of biopsy images in diagnosis is difficult and depends on the clinical experience of pathologists. However, the classification accuracy of computerized biopsy tissue images with similar lesions is low, and the problem of incomplete experimental objects needs to be addressed. This paper proposes a method of cervical biopsy tissue image classification based on least absolute shrinkage and selection operator (LASSO) and ensemble learning-support vector machine (EL-SVM). Using the LASSO algorithm for feature selection, the average optimization time was reduced by 35.87 seconds while ensuring the accuracy of the classification, and then serial fusion was performed. The EL-SVM classifier was used to identify and classify 468 biopsy tissue images, and the receiver operating characteristic (ROC) curve and error curve were used to evaluate the generalization ability of the classifier. Experiments show that the normal-cervical cancer classification accuracy reached 99.64%, the normal-low-grade squamous intraepithelial lesion (LSIL) classification accuracy was 84.25%, the normal-high-grade squamous intraepithelial lesion (HSIL) classification accuracy was 87.40%, the LSIL-HSIL classification accuracy was 76.34%, the LSIL-cervical cancer classification accuracy was 91.88%, and the HSIL-cervical cancer classification accuracy was 81.54%.

/pdf/classification-of-cervical-biopsy-images-based-on-lasso-and-3abpp13h9l.pdf

Classification of Cervical Biopsy Images Based on LASSO and EL-SVM

Generally, a spam image is an unsolicited message electronically sent to a wide group of arbitrary addresses. Due to attractiveness and more difficult detection, spam images are the most complicated type of spam. One of the ways to encounter the spam images is an optical character recognition, OCR, method. In this paper, the proposed enhanced HOG feature extraction method has been used so that the optical character recognition system of spam has been enhanced by using the HOG feature extraction method in such a way to be both resistant against the character variations on scale and translation and to be computationally cost-effective. For these purposes, two steps of the cropped image and input image size normalization have been added to pre-processing stages. Support vector machine, SVM, was employed for classification. Two heuristic modifications including thickening of the thin characters in the pre-processing stage and non-discrimination in detecting the uppercase and lowercase letters with the same shapes in the classification stage have been also proposed to increase the system recognition accuracy. In the first heuristic modification, when all pixels of the output image are empty (the character is eliminated), the original image was made thicker by one layer. In the second modification, when recognizing the letters, no differentiation was considered between the uppercase and lowercase letters with the same shapes. An average recognition accuracy of the modified HOG method with two heuristic modifications equals 91.61% on Char74K database. Then, an optimum threshold for classification was investigated by ROC curve. The optimal cutoff point was 0.736 with the highest average accuracy, 94.20%, and AUC, area under curve, for ROC and precision–recall, PR, curves were 0.96 and 0.73, respectively. The proposed method was also examined on ICDAR2003 database, and the average accuracy and its optimum using ROC curve were 82.73% and 86.01%, respectively. These results of recognition accuracy and AUC for ROC and PR curve showed an outstanding enhancement in comparison with the best recognition rate of the previous methods.

An efficient character recognition method using enhanced HOG for spam image detection

Reduction in the dimensions of silicon based devices has produced extraordinary developments in the performance of electronic systems. Recently, the advantages and challenges that caused by silicon devices shrinking have been investigated in many studies. Most of them have concluded that silicon technology is coming to an end and new innovations are required in the near future. Graphene is the promising candidate material as building blocks for nanoelectronic industry in order to silicon technology replacement. Graphene is first two dimensional materials that has significant potential in future nanoelectronic devices and other nanotechnology applications; therefore, it is an attractive subject for the researchers and scientists. Graphene is a carbon allotrope and is the basic element of other carbon allotropes. In this paper, main graphene production approaches are mentioned. The mechanical, thermal, optical, electrical, electronic and other fundamental properties of graphene are introduced. Moreover, it is considered as a novel material with numerous applications, which, we mention a few of important utilizations of graphene and its derivatives. Then, the different types of defects in graphene and their specifications, the methods of defect generation, defect healing, and properties of defective graphene are studied. Graphene nanoribbons and their geometric structures, fabrication approaches and electronic specifications are investigated. The effects of width, vacancy defect and doping concentration on the band structure of the graphene nanoribbons are extracted with ATK software in this paper. Simulation results showed that by increasing in the width, vacancy defect and doping concentration of nanoribbons, their band gap were decreased. These properties are suitable for controlling the channel of graphene nanoribbon field effect transistors.

Graphene properties and applications in nanoelectronic

Scene text detection and recognition: a survey

In nanoscale transistors, electron tunneling increases and causes a large leakage current due to the reduced channel length and gate oxide thickness. To reduce the short-channel effects and leakage current, the gate oxide can be selected from materials with high dielectric constant. In this paper, lanthanum aluminate (LaAlO3), a material with a high dielectric coefficient, is used as a gate dielectric in the structure of graphene nanoribbon field-effect transistors (GNRFET) and the effect of using this material on the device performance is studied. The transistor characteristics are extracted from device simulations performed by self-consistently solving the Poisson and Schrodinger equations in the nonequilibrium Green’s function formalism, employing the tight-binding approximation in the mode-space and ballistic regime. The ON-state and OFF-state currents, the ION/IOFF ratio, the threshold voltage, the drain-induced barrier lowering (DIBL), the subthreshold swing, and the transconductance (gm) are calculated for LaAlO3 as well as different materials as the gate dielectric to investigate the device performance. Based on the simulation results, the use of LaAlO3 as the gate dielectric in the transistor structure instead of other materials leads to larger values of the ON-state current, ION/IOFF ratio, and gm as well as smaller values of the OFF-state current, threshold voltage, DIBL, and subthreshold swing. According to the obtained results and excellent properties of LaAlO3, the use of this material could improve the performance of GNRFETs.

Hassan Khalesi

Papers

A novel pipeline framework for multi oriented scene text image detection and recognition

An efficient character recognition method using enhanced HOG for spam image detection

Graphene properties and applications in nanoelectronic

Scene text detection and recognition: a survey

Improving the performance of graphene nanoribbon field-effect transistors by using lanthanum aluminate as the gate dielectric