Bangla Handwritten Character Segmentation Using Structural Features: A Supervised and Bootstrapping Approach

In this article, we propose a new framework for segmentation of Bangla handwritten word images into meaningful individual symbols or pseudo-characters. Existing segmentation algorithms are not usually treated as a classification problem. However, in the present study, the segmentation algorithm is looked upon as a two-class supervised classification problem. The method employs an SVM classifier to select the segmentation points on the word image on the basis of various structural features. For training of the SVM classifier, an unannotated training set is prepared first using candidate segmenting points. The training set is then clustered, and each cluster is labeled manually with minimal manual intervention. A semi-automatic bootstrapping technique is also employed to enlarge the training set from new samples. The overall architecture describes a basic step toward building an annotation system for the segmentation problem, which has not so far been investigated. The experimental results show that our segmentation method is quite efficient in segmenting not only word images but also handwritten texts. As a part of this work, a database of Bangla handwritten word images has also been developed. Considering our data collection method and a statistical analysis of our lexicon set, we claim that the relevant characteristics of an ideal lexicon set are present in our handwritten word image database

Segmental K-Means Learning with Mixture Distribution for HMM Based Handwriting Recognition

This paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. The Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A deterministic clustering technique is used to estimate the initial parameters of an HMM. Bayesian information criterion (BIC) is used to select the topology of the model. The wavelet transform is used to extract features from a grey-scale image, and avoids binarization of the image

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Not AvailableA study was conducted to quantify the effect of elevated carbon dioxide (CO2) and temperature on soil organic nitrogen (N) fractions and enzyme activities in rice rhizosphere. Rice crop was grown inside the open top chambers in the ICAR-Indian Agricultural Research Institute. The N was applied in four different doses. Grain yield and aboveground N uptake by rice significantly reduced under elevated temperature. However, elevated CO2 along with elevated temperature was able to compensate this loss. Principal component analysis clearly indicated that microbial biomass carbon, microbial biomass N, amino acid N, total hydrolysable N, ammonia N and serine–threonine N contributed significantly to rice grain yield. Combined effect of elevated CO2 and elevated temperature decreased the total hydrolysable N, especially for lower N doses. The N-acetyl-glucosaminidase and leucine aminopeptidase enzyme activities were negatively correlated with the organic N pools. Higher activities of these enzymes under limited N supply may accelerate the decomposition of organic N in soil. When N was applied in super-optimal dose, plant N demand was met thereby causing lesser depletion of total hydrolysable N. Better nitrogen management will alleviate faster depletion of native soil N under future scenario of climate change and thus might cause N sequestration in soil.Not Availabl

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Not AvailableAnthropogenic activities in few decades past have increased the concentration of the atmospheric greenhouse gases (GHGs) which leads to climate change. This changing climate will certainly have impact on agricultural production. A study was carried out during the kharif season of year 2017 inside the open top chamber (OTCs) in IARI farm, New Delhi to quantify the interactive effect of elevated CO2 and temperature on growth of rice crop. Rice crop was grown in crates under two different CO2 levels: ambient (400 ppm) and elevated (550 ± 25 ppm) and with two temperature levels: ambient and elevated ( + 2°C). Growth of rice increased in elevated CO2 treatment while it decreased under high temperature condition. This was observed in terms of changes in tiller number, straw weight and root weight of the crop. Straw weight of rice reduced from 44.7 g hill - 1 to 52.1 g hill - 1in high temperature treatment. But increase in CO2 concentration significantly increased straw weight of the crop. The study showed that increased CO2 concentration was able to compensate the loss due to enhance growth of rice crop under high CO2 condition.Not Availabl