There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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)

This book brings together current knowledge of terrestrial C sequestration in Central Asia. The themes treated include: biophysical environments, water resources, sustainable agriculture, soil degradation, the effects of irrigation schemes on secondary salinization, soil management and its relationship to carbon dynamics; the relationship between forest management and carbon dynamics, economic analyses of land use practices, important methodological issues arising from the use of GIS, remote sensing, carbon budgeting and scaling, and a review of the knowledge gaps in carbon and climate change. The book is a reference source for soil, water, vegetation, climate, land use and management in the region. The book will be of interest to a wide variety of environmental scientists, economists and those interested in policy issues for the sustainable management of natural resources.

Climate Change and Terrestrial Carbon Sequestration in Central Asia

There are many applications of the handwritten character recognition (HCR) approach still exist. Reading postal addresses in various states contains different languages in any union government like India. Bank check amounts and signature verification is one of the important application of HCR in the automatic banking system in all developed countries. The optical character recognition of the documents is comparing with handwriting documents by a human. This OCR is used for translation purposes of characters from various types of files such as image, word document files. The main aim of this research article is to provide the solution for various handwriting recognition approaches such as touch input from the mobile screen and picture file. The recognition approaches performing with various methods that we have chosen in artificial neural networks and statistical methods so on and to address nonlinearly divisible issues. This research article consisting of various approaches to compare and recognize the handwriting characters from the image documents. Besides, the research paper is comparing statistical approach support vector machine (SVM) classifiers network method with statistical, template matching, structural pattern recognition, and graphical methods. It has proved Statistical SVM for OCR system performance that is providing a good result that is configured with machine learning approach. The recognition rate is higher than other methods mentioned in this Journal of Information Technology and Digital World (2021) Vol. 03/ No. 02 Pages: 92-107 https://www.irojournals.com/itdw/ DOI: https://doi.org/10.36548/jitdw.2021.2.003 93 ISSN: 2582-418X Submitted: 18.04.2021 Revised: 12.05.2021 Accepted: 1.06.2021 Published: 08.06.2021 research article. The proposed model has tested on a training section that contained various stylish letters and digits to learn with a higher accuracy level. We obtained test results of 91% of accuracy to recognize the characters from documents. Finally, we have discussed several future tasks of this research further.

https://irojournals.com/itdw/V3/I2/03.pdf

Construction of Statistical SVM based Recognition Model for Handwritten Character Recognition

Ascochyta blight (AB) caused by Ascochyta rabiei (Pass.) Labr. is an important and widespread disease of chickpea (Cicer arietinum L.) worldwide. The disease is particularly severe under cool and humid weather conditions. Breeding for host resistance is an efficient means to combat this disease. In this paper, attempts have been made to summarize the progress made in identifying resistance sources, genetics and breeding for resistance, and genetic variation among the pathogen population. The search for resistance to AB in chickpea germplasm, breeding lines and land races using various screening methods has been updated. Importance of the genotype × environment (GE) interaction in elucidating the aggressiveness among isolates from different locations and the identification of pathotypes and stable sources of resistance have also been discussed. Current and modern breeding programs for AB resistance based on crossing resistant/multiple resistant and high-yielding cultivars, stability of the breeding lines through multi-location testing and molecular marker-assisted selection method have been discussed. Gene pyramiding and the use of resistant genes present in wild relatives can be useful methods in the future. Identification of additional sources of resistance genes, good characterization of the host–pathogen system, and identification of molecular markers linked to resistance genes are suggested as the key areas for future study.

/pdf/an-update-on-genetic-resistance-of-chickpea-to-ascochyta-53jge3iush.pdf

An Update on Genetic Resistance of Chickpea to Ascochyta Blight

One hundred and seventy three germplasm lines/varieties of chickpea received from various research organizations were screened for the sources of resistance against chickpea blight disease (Ascochyta rabiei (Pass) Lab) by artificially inoculating the germplasm under a plastic tunnel where temperature, ranged from 8-24C and humidity was maintained above 80% by sprinkling fresh water. Out of 61 lines received form Pulses Research Institute, Faisalabad 3 lines viz., 03039, 03041 and 03053 exhibited highly resistant response while 11 lines viz., 03001, 03002, 03011, 03016, 03020, 03023, 03024, 03035, 03040, 03044 and 03045 displayed resistant reaction. The rest of the lines displayed moderately susceptible to susceptible reaction. Out of 80 lines received from Nuclear Institute of Agriculture and Biology, Faisalabad 5 test lines i.e. 03115, 03131, 03133, 03143 and 03159 were found to be highly resistant while 16 test lines viz., 03108, 03126, 03127, 03153, 03156, 03157, 03161, 03162, 03169, 03170, 03172, 03173, 03174, 03177, 03178 and 03180 were found resistant against A. rabiei infection. Similarly out of 90 test germplasm lines/ cultivars received from Arid Zone Research Institute, Bukhar 3 lines viz., 93A-086, 93A-111 and 93A-3354 exhibited highly resistant response while 5 lines viz., 91A-016, 92A-792, 92A-117, 96A-4504 and NES-98K4, displayed resistance response against A. rabiei infection. These resistant sources can further be exploited in breeding programe for the development of disease resistant commercial cultivars.

Sources of resistance in chickpea germplasm against ascochyta blight

Urdu Nasteleeq text recognition is one of the very challenging problems in document image processing. The cursive nature of Urdu script makes character segmentation very difficult. Therefore, most of the researchers have shifted the focus on segmentation free approaches based on Urdu ligatures. In most cases, these ligatures are characterized using complicated and extensive feature extraction techniques. These features might fail to capture the minor details and hence lead to the loss of useful information. This study proposes the use of Convolutional Neural Networks for recognition of Urdu ligatures. Such deep learning techniques are novel and fast as compared to the conventional feature extraction methods. The input to the system are fixed size ligature images. The system automatically extracts features from raw pixel values of these images. The system evaluated on 18,000 Urdu ligatures with 98 different classes realized a recognition rate of up to 95%.

Classification of Urdu Ligatures Using Convolutional Neural Networks - A Novel Approach

Screening of rice germplasm against blast disease caused by Pyricularia oryzae Cav.

Phytophthora cinnamomi? - a cause of shisham decline in Punjab, Pakistan.

In order to see the impact of environmental conditions on Urdbean leaf crinkle disease development, 20 genotypes viz., 6022-22, 95009, 6036-12, 96 CM-016, AARIM-32, AARIM-164, M-17, M-3, 95024, Mash-88, 6036-7, ES-1, NCH-3-3, 6036-7, Mash-1, 4 CM-717, IAM 33-40, 3 CM-706, 6036-14 and Mash-97 from the reaction groups viz. MR, MS, S and HS were sown for 4 seasons (spring and summer) during 2005-2006. It was observed that disease incidence differed in each growing season whereas it showed overall significant correlation with maximum and minimum temperature and no correlation with relative humidity, rainfall and wind speed in each growing season, respectively. When data were split by genotypes the level of correlation decreased. To characterize the environmental conditions conducive for the disease epidemics, it was found that maximum ULCV disease severity/incidence was recorded at 35-42 o C (maximum temperature) and 21-29 o C (minimum temperature). There was absolutely no correlation with weekly relative humidity, rainfall and wind movement.

N. Javed

Papers

Sources of resistance in chickpea germplasm against ascochyta blight

Classification of Urdu Ligatures Using Convolutional Neural Networks - A Novel Approach

Screening of rice germplasm against blast disease caused by Pyricularia oryzae Cav.

Phytophthora cinnamomi? - a cause of shisham decline in Punjab, Pakistan.

Characterization of environmental factors conducive for urdbean leaf crinkle (ulcv) disease development