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One of the most essential factors in classifying and qualitatively evaluating mangoes for various industrial uses is weight. To meet grading requirements during industrial processing, this paper presents an orientation-independent weight estimation method for the mango cultivar “Alphonso.” In this study, size and geometry are considered as key variables in estimating weight. Based on the visual fruit geometry, generalized hand-crafted local and global features, and conventional features are calculated and subjected to the proposed feature selection methodology for optimal feature identification. The optimal features are employed in regression analysis to estimate the predicted weight. Four regression models –MLR, Linear SVR, RBF SVR, and polynomial SVR—are used during the experimental trials. A self-collected mango database with two orientations per sample is obtained using a CCD camera. Three different weight estimation techniques are used in the analysis concerning orientation 1, orientation 2, and combining both orientations. The SVR RBF kernel yields a higher correlation between predicted and actual weights, and experiments demonstrate that orientation 1 is symmetric to orientation 2. By exhibiting a correlation coefficient of R2 = 0.99 with SVR-RBF for weight estimation using both orientations as well as individual orientations, it is observed that the correlation between predicted and estimated weights is nearly identical

Low resolution mobile photographed images pose a complex set of research challenges as compared to non-mobile captured images, which really is a significant issue these days. For non-mobile captured and high-resolution photos, current plant recognition systems are the best solution providers. This study proposes the identification and extraction of leaf regions from complex backgrounds to meet the automatic recognition needs of a variety of mobile phone users. Additionally multiple factors complicate the leaf region extraction from complex backgrounds such as varying background patterns, clutters, varying leaf shape/size and varying illumination due to volatile weather conditions. In this paper, a simple and efficient method for leaf extraction from complex background of mobile photographed low resolution images is proposed based on color channel thresholding and morphological operations. A self-built database of 5000 mobile photographed images in realistic environments is adapted for experimentations. Experiments were conducted on various resolution categories, and it was discovered that the proposed model has an average dice similarity measure of 99.5 percent for successful extraction of the leaf region in 13MP mobile photographed images. Furthermore, our comparative investigation reveals that the suggested model outperforms both traditional and state-of-the-art techniques.

Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1–17 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK.

Handwriting style of an individual will be usually varying from other individuals. The degree of complexity involved in recognition of a handwritten character of south Indian languages is high due to the presence of compound characters. Furthermore the complexity of interpreting handwritten text aggravates due to irregular handwriting styles. Building of datasets for training the database in such environments is tremendous and requires a lot of manual data processing and other preliminary tasks. The major contribution focuses on creation of morphable feature space for addressing handwritten recognition problem. In this paper, techniques useful for simplifying dataset preparation procedures along with a hybrid recognition approach for improvising recognition accuracies of Telugu handwritten characters are proposed. The techniques include text document segmentation, automatic cropping and pincushion and distance transformation based recognition approach. A feature vector is generated from the normalized Gabor features are extracted from pincushion and distance transform models of a character image and classified using Ada boost classifier with a recognition accuracy of 89%.

In this work, the evaluation of segmentation techniques towards the detection tumours or gliomas on BraTs 2018 data sets on T2 weighted 3D MRI images is carried out. Techniques experimented include pixel wise Linear Binary Pattern (LBP), region growing and Otsu thresholding to compare the efficiency of the same with proposed approach. In this work, 3D MRI images are initially subject to slicing from which about 10% from the top and bottom are normalized as those slices does not possess any significant details (empty slices). Further, the enhancement of tumour/gliomas regions is performed on normalized slices using high boost ‘convolution kernel processing in various trials with varying kernel sizes of high boost convolution kernel. The glioma regions enhanced using kernel are subject to local adaptive thresholding technique for extraction of gliomas from normalized slices. The detected gliomas are compared with the results of ground truth segmentation to determine the effectiveness of the proposed technique. It is observed in 80% of slices are successful in detection of gliomas both in case of Low Grade Glioma (LGG) and High Grade Glioma (HGG). By analyzing the results, it is observed that proposed method has outperformed with average Jaccard and Dice similarity recorded as 87.20% and 89.18% for HGG and where as in case of LGG it is found to be 83.77% and 87.74%.

Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize Kannada handwritten characters. Capsule network architecture is built of an input layer, two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer. For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%.

The proposed research aims to restore deteriorated text sections that are affected by stain markings, ink seepages and document ageing in ancient document photographs, as these challenges confront document enhancement. A tri-level semi-adaptive thresholding technique is developed in this paper to overcome the issues. The primary focus, however, is on removing deteriorations that obscure text sections. The proposed algorithm includes three levels of degradation removal as well as pre- and post-enhancement processes. In level-wise degradation removal, a global thresholding approach is used, whereas, pseudo-colouring uses local thresholding procedures. Experiments on palm leaf and DIBCO document photos reveal a decent performance in removing ink/oil stains whilst retaining obscured text sections. In DIBCO and palm leaf datasets, our system also showed its efficacy in removing common deteriorations such as uneven illumination, show throughs, discolouration and writing marks. The proposed technique directly correlates to other thresholding-based benchmark techniques producing average F-measure and precision of 65.73 and 93% towards DIBCO datasets and 55.24 and 94% towards palm leaf datasets. Subjective analysis shows the robustness of proposed model towards the removal of stains degradations with a qualitative score of 3 towards 45% of samples indicating degradation removal with fairly readable text.

Document image enhancement is one of the prime researches in the area of optical character recognition and computer vision. Preprocessing procedure for a document depends on document layout, aging and document material type. This paper proposes a preprocessing technique for the enhancement of ancient and degraded document images. Initially the degraded patches from the document image is collected and used for learning through a variational de-noising autoencoder followed by document image enhancement. Ground truth images of the degraded patches are trained with the help of an adamax optimizer. A deep learning architecture comprised of five levels of convolution is devised for encoding and decoding process. Down sampling is initially performed in the encoding stage after each level of convolution. Further up sampling is conducted in the decoding stage. Experimentations are conducted on DIBCO (2016, 2013, 2012, 2011, 2010 and 2009) datasets and the results of enhancement are found to be promising with an average RMSE of 0.106 for batch size 1 and 24 epochs.

Segmentation of mango fruit from the images is one of the challenging research problem. This work explores the various segmentation techniques employed for extraction of mango fruit region from variety of complex backgrounds in images. Extracting this green fruit from its complex background has always posed a challenge.Semi automated approach has been proposed that involves selection of appropriate clusters followed by morphological operations, identifying connected components and displaying image with largest component.However, few methods have been experimented in this area to explore performance of the segmentation algorithms. Implication of segmentation algorithms used in the past and their efficiencies are analyzed and interpreted in our study so as to assist researchers working in this area. The merits and limitations of every algorithm are also mentioned. The performance metrics of the proposed approach and other segmentation algorithms is compared with respect to the execution time and inferences with regard to the same has been explored.

Document Image Processing is considered one of the widely used technologies today. It has proved to be extremely useful in digitizing physical documents to preserve them for longer times. The primary challenge in this work is to extract the overlapping lines from handwritten document images and table detection from pre-printed documents. In this research paper, we present a line segmentation and table detection algorithm suitable for pre-printed and handwritten documents. Initially, the input document image is preprocessed and a baseline is created for offline every peak to carry out line segmentation. Further, table detection is accomplished with the use of edge detection techniques and morphological operations

Throughout this article, we are researching two distinct algorithms to separate animals from animal pictures. Since animals appear in a very complex background and often surrounded by greenery, segmenting an animal from its background is a very challenging task. Animal segmentation further helps the problem of animal identification and classification. Here the animals are segmented using graph cut and similarity region merging techniques. To determine the efficiency of our process, an analysis is carried out on our own dataset of 50 animal types, comprising 5000 images. The various performance measures such as Information Variation, Global Consistency Error, Probabilistic Rand Index, and Boundary Displacement Error are used for the purpose of assessment.

Fruit classification has always been a challenging task as it involves sorting, grading, and analyzing fruit quality specifically for import, consumption purposes and for workers at the Point of Sale (POS) in supermarkets. The proposed work has used computer vision and support vector machine (SVM), for classification. Among the fruit images collected, 505 fruit images are used in training and 150 fruit images are used in testing. The work is carried out in various stages. First, the input image is resized to 256×256 resolutions. Later, pre-processing is performed using Gaussian filter to enhance the image quality by reducing the noise. Feature space is created by extracting the colour, texture and shape features. Then, principal component analysis (PCA) is applied to reduce the dimensions of the feature space to overcome the curse of dimensionality. Further, the support vector machine (SVM) classifier is used for training the data. Total of 655 images distributed across 18 categories of fruits are maintained that achieves 87.06% accuracy.

In this work, a novel model for recognition of handwritten Kannada characters using transfer learning from Devanagari handwritten recognition system is presented. The objective is to use the knowledge of large data corpus of Devanagari recognition system as training data to perform the recognition of handwritten Kannada characters that has a smaller data corpus. The transfer of knowledge for recognition is carried out using deep learning network architecture to VGG19 NET. VGG19 NET is defined of five blocks of hidden layers, two dense fully connected layers and an output layer. Each block (except block1) consists of four convolution layers along with a max pooling layer. In proposed classification framework, Devanagari character set consists of totally 92000 images with 46 classes and Kannada character set is built with 81654 for training and 9401 for testing, for about 188 classes with each class comprising of 200–500 sample image. A total of 1,23,654 data samples is employed for training with VGG19 NET. For experimentation 9401 samples of about 188 classes built of about 40–100 samples in each classes is used and for which accuracy close to 90% is achieved. Validated accuracy after evaluation in 10 epochs with VGG19 NET, it has recorded an accuracy of 73.51% with a loss of 16.18%.

A lot of attention has emerged regarding the aspects of text detection and identification as OCR has generated a lot of prominence over the years. There has been a number of experiments conducted in this field to make the results more and more accurate. Most of the experiments carried out have paid attention to only a few attributes and not a lot of trails have been done for unusual scenarios, like a lot of techniques produces accurate results only for horizontal textual orientation. So there should different techniques for analyzing such images which have a complex background, different font styles, colors, textual orientations. Text detection on images containing texts of different orientations, different font types, and images with complex backgrounds is taken for the proposed work. There are mainly 3 steps in the algorithm proposed, the Canny edge detection approach for gradient filtering is applied in the first stage to detect the skeletal structure of various objects in the image. In the next stage textual threshold-based object filtering is carried out using the convolution technique with a heuristic thresholding model. The textual object filtering after convolution is subject to the last stage called post enhancement technique. In this stage, partial non-textual objects being filtered out are employed for removal based on geometrical properties of gradients of images, thus retaining only the textual objects. Finally, the textual object filtered gradient image is considered as a mask image for mapping it to the original image for text detection. Experimentations are conducted on Google Street View Datasets for which a subjective evaluation procedure is adapted to validate the results resulting in promising outcomes for more than 50% of images.

Handwritten script recognition and classification is one of the unsolved challenges in an area of offline character recognition. In this paper, handwritten script classification is performed on different South Indian scripts Kannada, Telugu, Tamil, and Malayalam along with Devanagari and Roman Scripts. The proposed model for handwritten script classification initiates with a knowledge repository of word-level and character-level handwritten data samples that are collected from more than 500 writers and extracted using, projection histogram features of documents. Datasets are extracted at the character and word level separately and forwarded for wavelet feature analysis, using DB-3, DB-4, Haar, and Symlet, and the feature vectors are modeled for character level and word level datasets. These features are used for classification which is conducted separately for character and word level wavelet features. The features are employed separately for wavelets DB3, DB4, Haar, and Symlet during classification. A comparative analysis is conducted using a directional gradient feature which has shown dominant results towards printed script classification in literature. Classification is conducted using Autoencoders for both wavelet features and directional gradient features, along with that, the efficiency of conventional classifiers like support vector machines, KNN, and boosted trees is also tested with wavelet features. In the proposed method, Symlet features provide efficient performance with Autoencoders compared to other features with 70.61% accuracy.

Automated diagnosis of diabetic retinopathy is one of the major goals of hemorrhage detection. Occurrence of hemorrhages in retinal images indicates the early signs of person being suffering from diabetic retinopathy. In this paper, techniques for segmentation and detection of hemorrhages from retinal images are proposed. The objectives of this work primarily focuses of extraction of blood vessel structures and hemorrhages using connected object and Sobel edge detection techniques on green channel extracted from RGB and further classify the local binary pattern features from segmented objects as hemorrhages detected and non-hemorrhage detected images. Pre-processing is performed on retinal images by extraction of green channel images being subject to adaptive histogram equalization, Laplacian filtering followed by morphological bridging operation. Further hemorrhage detection is carried out in both in machine learning and non machine learning approaches. The datasets employed for experimentation is acquired from Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy. A comparative study on the outcomes of both the method is conducted and observed that the results of both methods are in par with each other with promising average accuracy of about 92.31%

The field of Document Image Processing has encountered sensational development and progressively across the board relevance lately. Luckily, propels in PC innovation have kept pace with the fast development in the volume of picture information in different applications. One such utilization of Document picture preparing is OCR (Optical Character Recognition). Pre-preparing is one of the pre-imperative stages in the handling of record pictures which changes the archive to a frame reasonable for ensuing stages. In this paper, various preprocessing techniques are proposed for the enhancement of degraded document images. The algorithms implemented are adept at handling variety of noises that include foxing effect, illumination correction, show through effect, stain marks, and pen and other scratch marks removal. The techniques devised works based on noise degradation models generated from the attributes of noisy pixels which are commonly found in degraded or ancient document images. Further, these noise models are employed for the detection of noisy regions in the image to undergo the enhancement process. The enhancement procedures employed include the local normalization, convolution using central measures like mean and standard deviation, and Sauvola’s adaptive binarization technique. The outcomes of the preprocessing procedure is very promising and are adaptable to various degraded document scenarios.

This paper presents a simple and efficient approach to perform fruit detection and predict nutrition information of the fruits using deep Alex networks (DAN). The datasets employed for analysis are acquired from fruit 360 database of image processing challenges. Fruit categories include apples, berries, banana, grape, papaya, peach, avocado, and multiple flavors of apple. And also, the experimentations are carried out on various other fruit samples collected from multiple Web repositories. The network architecture is as usual comprised of to five convolution layers and three fully connected layers including the max pooling, RELU layers. The input images are assumed to be of dimensions 227 × 227 × 3 with number of filters of 96 of size 11 × 11 × 3 with a stride length of 4. The results of experiment prove that fruit detection using DAN is efficient with an accuracy of about 91% to classify the fruits of about 50 different categories on a single machine of configurations 1 GPU, 8 GB RAM, and octa-core processor.

Object segmentation and classification in the video sequence is a classical and critical problem that is constantly addressed due to vast real-time applications such as autonomous vehicles, smart surveillance, etc. Segmentation and classification of moving objects in video sequences captured from real-time non-constraint sequences is a challenging task. This paper presents a moving object segmentation and classification method for video sequences captured in a real-time environment. The key contributions of the paper include a method to detect and segment motion regions by applying the non-parametric Kolmogorov–Smirnov statistical test in the Spatio-temporal domain and a probabilistic neural network-based classification method to classify the moving objects into various classes. Promising results are obtained by experimentation using challenging PETS and Change Detection datasets. To corroborate the efficacy, a comparative analysis with contemporary method is also performed.

Achieving high accuracies in recognition of handwritten text is a challenging research problem and never exhausting. The factors that instill challenges in handwritten character recognition include high degree of variability in writing, script type and the type of documents etc. In this paper, we focus on recognition of handwritten Telugu text commonly found in document images. The character set include all the vowels, consonants and single level vowel consonant clusters chosen in accordance with the commonly used terminology employed in composition pre-printed documents such as admission forms. In this paper, an algorithm is devised that performs zone-based feature extraction of the segmented character images. In the proposed work, the Gabor features are extracted from the character image at zone level and its efficiency is evaluated individually on two different zone representations and entire image at various scales and orientations. The classification and recognition performance is analyzed using nearest neighbor classifier, Naive Bayesian, multi-class SVM and probabilistic neural networks classifiers. The efficiency of the classifiers are also tested with statistical, Histogram of Gradients and Hu moments‟ feature extraction methods and the best accuracy of the system is found to be 84.8% for Gabor features with zone representation 2 and with multi-class SVM classifier.

Precise automatic reading of the characters in a document image is the functionality of Optical Character Recognition (OCR) systems. The overall recognition accuracy can be accomplished only through efficient pre-processing procedures. The recognition of characters in pre-printed document images is a highly challenging task as it desires unique pre-processing methods and it depends on the layout of document. In this paper we propose a pre-processing technique for removal of horizontal/vertical lines in the pre-printed documents. The major challenge involved in removal of the horizontal lines is retention of the pixels overlapped between line and characters in document. The proposed algorithm works in two phases; image enhancement and line detection is made in the first phase and the second phase comprises convolution process using rectangular structuring element for detection of text stroke crossings on lines which are detected in phase one. The output image is further subjected to undergo post enhancement and analysis operations using connected component analysis and area features for removal of broken/dotted line structures. The experimental outcomes achieved are quite satisfactory and consistent enough for subsequent processing of document.

Pre-processing is one of the significant protocol to be executed for achieving precise recognition of contents from aged document images. The aged document images are heavily besieged with the noisy artifacts accumulated due to its sustainability for longer times. In this paper, a technique is proposed for detection of variable regions from aged documents by employing the object detection technique and further histogram of gradients features are fed to the nearest neighbor classifier. The performance of the algorithm is analyzed with standard datasets of Tobacco800 Complex Document Image Database of University of Maryland Institute for Advanced Computer Studies.

Classification of handwritten and printed text in pre-printed documents enhances the performance of optical character recognition technologies. The objective of work presented lies in devising an approach to perform automatic classification of printed and handwritten text at word level, which is inherently found in pre-printed documents. The proposed work consists of three stages to perform the classification of printed and handwritten words in Telugu pre-printed documents. The stage one encompasses the feature computation from the segmented words, stage two determines text discrimination coefficient, and finally, the classification of printed and handwritten text using a decision model is accomplished in stage three. The statistical and geometrical moment features are computed with respect to the text block under consideration, and furthermore, these features are employed for determination of text discrimination coefficient. The results of experimentation are proved to be promising and robust with an accuracy of around 98.2 %.

Enhancement of document images is an interesting research challenge in the process of character recognition. It is quite significant to have a document with uniform illumination gradient to achieve higher recognition accuracies through a document processing system like Optical Character Recognition (OCR). Complex document images are one of the varied image categories that are difficult to process compared to other types of images. It is the quality of document that decides the precision of a character recognition system. Hence transforming the complex document images to a uniform illumination gradient is foreseen. In the proposed research, ancient document images of UMIACS Tobacco 800 database are considered for removal of marginal noise. The proposed technique carries out the block wise interpretation of document contents to remove the marginal noise that is present usually at the borders of images. Further, Hu moment’s features are computed for the detection of marginal noise in every block. An empirical analysis is carried out for classification of blocks into noisy or non-noisy and the outcomes produced by algorithm are satisfactory and feasible for subsequent analysis.

Starch, composed of amylose and amylopectin, greatly influences rice cooking and textural quality, which in turn is controlled by various isoforms of several enzymes. Activity of one or more isoforms of starch-synthesizing enzymes results in various forms of starch structure based on the amylopectin chain length and average external, internal and core chain length distribution and hence results in varying physicochemical and cooking quality. Since the synthesis of starch is highly complex, it is crucial but essential to understand its biosynthetic pathway, starch structure and effects on the physicochemical properties that control eating and cooking quality, and alongside conduct research on gene/QTL mapping for use in marker-assisted selection (MAS) with a view to improve and select cultivars with most desirable range and class of rice starch properties. This article presents the updates on current understanding of the coordination among various enzymes/isoforms towards rice starch synthesis in endosperm and their effect on rice grain physicochemical, cooking and eating qualities. The efforts in identifying regions responsible for these enzymes by mapping the gene/QTLs have provided a glimpse on their association with physicochemical and cooking properties of rice and, hence, improvement is possible by modifying the allelic pattern, resulting in down or nil regulation of a particular enzyme. The clear understanding of the tissue specific coordination between enzyme isoforms and their subsequent effect in controlling eating and cooking properties will enhance the chances to manipulate them for getting desired range of amylose content (AC) and gelatinization temperature (GT) in improved cultivars through combining desired alleles through MAS.

Bacterial blight (BB) is a major production threat to Basmati, the aromatic rice prized for its unique quality. In order to improve the BB resistance of two elite, traditional BB-susceptible Basmati varieties (Taraori Basmati and Basmati 386), we utilized the strategy of limited marker-assisted backcrossing for introgression of two major BB resistance genes, Xa21 and xa13, coupled with phenotype-based selection for improvement of their plant type and yield. Improved Samba Mahsuri, an elite high-yielding, fine-grain-type BB-resistant rice variety served as donor for BB resistance. Backcross-derived improved Basmati lines at BC1F5 possessing a single resistance gene (i.e. either Xa21 or xa13) displayed moderate resistance to BB, while lines possessing both Xa21 and xa13 showed significantly higher levels of resistance. Two-gene pyramid lines (Xa21 + xa13) possessing good grain and cooking quality similar to their respective traditional Basmati parents, short plant stature (

The appearance and cooking quality of rice determine its acceptability and price to a large extent. Quantitative trait loci (QTLs) for 12 grain quality traits were mapped in 2 mapping populations derived from Oryza sativa cv Swarna × O. nivara. The BC(2)F(2) population of the cross Swarna × O. nivara IRGC81848 (population 1) was evaluated during 2005 and that from Swarna × O. nivara IRGC81832 (population 2) was evaluated during 2006. Linkage maps were constructed using 100 simple sequence repeat (SSR) markers in population 1 and 75 SSR markers in population 2. In all, 21 QTLs were identified in population 1 (43% from O. nivara) and 37 in population 2 (38% QTLs from O. nivara). The location of O. nivara-derived QTLs mp1.2 for milling percent, kw6.1 for kernel width, and klac12.1 for kernel length after cooking coincided in the 2 populations and appear to be useful for Marker Assisted Selection (MAS). Four QTLs for milling percent, 1 QTL each for amylose content, water uptake, elongation ratio, 2 QTLs for kernel width, and 3 QTLs for gel consistency, each explained more than 20% phenotypic variance. Three QTL clusters for grain quality traits were close to the genes/QTLs for shattering and seed dormancy. QTLs for 4 quality traits were associated with 5 of the 7 major yield QTLs reported in the same 2 mapping populations. Useful introgression lines have been developed for several agronomic traits. It emerges that 40% O. nivara alleles were trait enhancing in both populations, and QTLs for grain quality overlapped with yield meta-QTLs and QTLs for dormancy and seed shattering.

Broadening of the genetic base for identification and transfer of genes for resistance to insect pests and diseases from wild relatives of rice is an important strategy in resistance breeding programs across the world. An accession of Oryza nivara, International Rice Germplasm Collection (IRGC) accession number 105710, was identified to exhibit high level and broad-spectrum resistance to Xanthomonas oryzae pv. oryzae. In order to study the genetics of resistance and to tag and map the resistance gene or genes present in IRGC 105710, it was crossed with the bacterial blight (BB)-susceptible varieties ‘TN1’ and ‘Samba Mahsuri’ (SM) and then backcrossed to generate backcross mapping populations. Analysis of these populations and their progeny testing revealed that a single dominant gene controls resistance in IRGC 105710. The BC1F2 population derived from the cross IRGC 105710/TN1//TN1 was screened with a set of 72 polymorphic simple-sequence repeat (SSR) markers distributed across the rice genome and the resistance gene was coarse mapped on chromosome 7 between the SSR markers RM5711 and RM6728 at a genetic distance of 17.0 and 19.3 centimorgans (cM), respectively. After analysis involving 49 SSR markers located between the genomic interval spanned by RM5711 and RM6728, and BC2F2 population consisting of 2,011 individuals derived from the cross IRGC 105710/TN1//TN1, the gene was fine mapped between two SSR markers (RMWR7.1 and RMWR7.6) located at a genetic distance of 0.9 and 1.2 cM, respectively, from the gene and flanking it. The linkage distances were validated in a BC1F2 mapping population derived from the cross IRGC 105710/SM//2 × SM. The BB resistance gene present in the O. nivara accession was identified to be novel based on its unique map location on chromosome 7 and wider spectrum of BB resistance; this gene has been named Xa33. The genomic region between the two closely flanking SSR markers was in silico analyzed for putatively expressed candidate genes. In total, eight genes were identified in the region and a putative gene encoding serinethreonine kinase appears to be a candidate for the Xa33 gene.

Digitization and automatic interpretation of document images into editable document format is the primary inclination of optical character recognition systems (OCR). This paper proposes a novel technique for resolution of post processing errors that occurs with respect to Telugu OCR using word level Unicode Approximation Models (UAM) through a mapper module. The mapper module performs the word level one-one mapping of assigning a sequence of recognized class labels to appropriate UAM. The sequence of recognized class labels are related to one particular word and are generated from the classifier as output. The proposed algorithm effectively resolves the problem of segmentation errors, preprocessing errors like cuts and merges in characters, noise, occlusions, semantic ordering and confusing character classes. The proposed UAM models provide adequate and consistent accuracies of around 96.2% for printed words and 91.7% towards handwritten words respectively.

Depending on the position on the panicle, grain filling differs for spikelets on primary branches and secondary branches of upper and lower portions in rice (Oryza sativa L.). With the objective of identifying candidate genes associated with the grain-filling process across the panicle, a candidate gene (CG)-based mapping approach was attempted in an F2 population derived from the cross between Rasi, a rice variety known for its good grain filling, and IC114927, a local landrace. The grain filling (%) was characterized for 444 F2 individual plants across the panicle. Within the F2 population, thirteen plants were identified with >90% grain filling across the panicle. For 120 markers designed from 1 kb upstream and within the candidate genes reported to be involved in translocation process of sugars, 18.3% polymorphism was observed. Using a selective genotyping approach comprising 24 individuals from eight groups based on their filling and position, two associated CG markers were identified. Further analysis of the mapping population showed a CG marker based on the sucrose phosphate synthase gene on chromosome 2 to be significantly associated with filling of grains on primary branches of the upper half of the panicle and another CG marker based on the transporter gene on chromosome 11 to be associated with filling of grains on primary branches of the lower half; secondary branches of the upper half and lower half of the panicle. Both the positive alleles were contributed by the variety Rasi. With a candidate gene-based marker mapping approach, two genes involved in sugar translocation and transport were identified to be directly associated with grain filling in rice.

Rice blast is one of the most devastating diseases affecting the rice crop throughout the world. In molecular breeding for host plant resistance, functional markers are very useful for enhancing the precision and accuracy in marker-assisted selection (MAS) of target gene(s) with minimum effort, time and cost. Pi54 (which was earlier known as Pikh) is one of the major blast resistance genes and has been observed to show resistance against many isolates of the blast pathogen in India. The gene has been cloned through map-based strategy and encodes a nucleotide-binding site–leucine-rich repeat (NBS–LRR) domain-containing protein. In the present study, we carried out allele mining for this gene and identified a 144-bp insertion/deletion (InDel) polymorphism in the exonic region of the gene. A PCR-based co-dominant molecular marker targeting this InDel, named Pi54 MAS, was developed. Pi54 MAS was observed to perfectly co-segregate with blast resistance in a mapping population with no recombinants. Validation of this marker in 105 genotypes which are either susceptible or resistant to rice blast disease showed that the marker is polymorphic in most of the resistant–susceptible genotype combinations and is more accurate than the earlier reported markers for Pi54. Hence this functional, co-dominant marker is suggested for routine deployment in MAS of Pi54 in breeding programs.