Palm leaves are the earliest forms of documentation for literature, showcasing rich traditions, philosophical insights, and scientific traditions in areas such as mathematics, medicine, agriculture, ...and martial arts, among others. This paper presents a deep semantic binarization network for enhancing 700-year-old Malayalam palm leaf manuscripts by addressing challenges such as uneven illumination, ink bleeds, stain marks, and brittleness. The learning model is trained with the ground truth data created using self-collected Malayalam palm leaf manuscripts, the Shiju Alex, and AMADI LONTAR degraded palm leaf manuscripts. The learning models are created by employing hyperparameter specifications of a fixed batch size of 32 with a learning rate of 0.00001, with epochs ranging from 100 to 500. Each learning model is analyzed by evaluating its performance using the proposed model, basic U-Net, and Sauvola Net on the datasets of AMADI LONTAR, Shiju Alex, and self-collected Malayalam manuscripts. The quantitative evaluation results show that the proposed model outperforms U-Net and the Sauvola Net models by achieving 90.55%, 0.205, and 90.44 of Accuracy, RMSE, and F-Measure towards validation set of self-collected datasets with batch sizes of 32 and 500 epochs. The data growth study conducted with varying training sample sizes shows a consistent increase in performance by the proposed model by achieving an accuracy of 90.55%, 88.26% precision, 70% recall, and 79% F-score towards validation of three datasets, demonstrating the effectiveness of the proposed method.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The method for document image classification presented in this paper mainly focuses on six different Malayalam palm leaf manuscripts categories. The proposed approach consists of three phases: ...dataset analysis, building a bag of words repository followed by recognition and classification using a voting approach. The palm leaf manuscripts are initially subject to pre-processing and subjective analysis techniques to create a bag of words repository during the dataset analysis phase. Next, the textual components from the manuscripts are extracted for recognition using Tesseract 4 OCR with default and self-adapted training sets and a deep-learning algorithm. The Bag of Words approach is used in the third phase to categorize the palm leaf manuscripts based on textual components recognized by OCR using a voting process. Experimental analysis was done to analyze the proposed approach with and without the voting techniques, varying the size of the Bag of Words with default/self-adapted training datasets using Tesseract OCR and a deep learning model. Experimental analysis proves that the proposed approach works equally well with/ without voting with a bag of words technique using Tesseract OCR. It is noticed that, for document classification, an overall accuracy of 83% without voting and 84.5% with voting is achieved with an F-score of 0.90 in both cases using Teserract OCR. Overall, the proposed approach proves to be high generalizable based on trial wise experiments with Bag of Words, offering a reliable way for classifying deteriorated Malayalam handwritten palm manuscripts.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
Highlights
This work presents a semi-adaptive binarization technique for ancient image enhancement.
Main focus of this work is to restore obscured text sections.
Multi-level thresholding approach is used for the removal of degradations.
Gradient of the original image is used in the computation of reference image to detect deteriorated text sections.
Pseudo-colouring and post-enhancement process finally transform to the enhanced image.
DIBCO and palm leaf document samples are used for experimentations.
Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of ...an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process. Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image. A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female. The experimentations are conducted on the datasets of Radiological Society of North America (RSNA) of about 12442 images. Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%. Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.
The realization of high recognition rates of degraded documents such as palm leaf manuscripts primarily relies on document enhancement. Advancement of deep learning models in the process of document ...enhancement plays a major role among non-deep learning models or thresholding methods. Preparation of readily available ground truth data for creation of deep learning models is of paramount importance as it is highly time consuming task. The ground truth dataset preparation involves greater complexities as ancient documents are affected with degradations such as fungi, humidity, uneven illumination, discoloration, holes, cracks, and other damages. We propose a Handwritten Malayalam Palm Leaf Manuscript Dataset (HMPLMD) and its ground truth data aspiring for advancements in the field of palm leaf image analysis. We employ the palm leaf manuscripts of Kambaramayanam and Jathakas for the sake of experimentations. The proposed ground truth samples of degraded palm leaves plays a crucial role in creation of specialized deep/transfer learning models to handle challenges related to binarization.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The restoration of degraded document images is a critical aspect of document image processing. In this study, we present a rigorous comparative analysis of two denoising techniques applied to ...degraded document images within the HSV and UV color spaces. The primary objective is to quantitatively assess the efficacy of these methods in enhancing the quality of degraded document images. Experiments were conducted using an extensive dataset of degraded document images, and a comprehensive evaluation was performed employing established metrics, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean square error (MSE). Results unequivocally demonstrate the superiority of the UV-based approach over the HSV-based method across all evaluation criteria. Specifically, the UV-based approach consistently outperforms the HSV-based approach in PSNR, SSIM, and MSE assessments. Qualitative analysis further underscores the remarkable effectiveness of both techniques in elevating the quality of degraded document images. This study addresses a pressing problem in document image processing by delivering invaluable insights into the comparative efficacy of these denoising techniques. It offers a useful tool for academics and professionals who want to improve the clarity and legibility of deteriorated document images.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The protein coding and functional regions in DNA sequences has become an exciting task in bioinformatics. In particular, the coding region has a 3-base periodicity, which helps for exon ...identification. Many signal processing tools and techniques have been successfully applied to identify tasks, but still need to be improved in this direction. In our work, we employ ANN classifier to predict coding and functional region of proteinin human embryo cell protein in first trimester, and evaluate their performances according to the comparison energy levels of coding region. The obtained from the threshold energy level, results show that in a box plot finally predict the mutation.
There are a number of methods to spot orthologous genes from homologous genes. Since identifying orthologous genes are main problem and play a major role in the hematologic genetic disorders. In this ...paper, we propose different approaches to discover orthologs of homologous hereditary diseases of the hematological system and find the evolutionary relationship between hereditary genetic diseases of the hematological system by adjoining the joining method of contiguous merging trees.
Computer-aided drug design (CADD) is designing a drug with the help of computational algorithms. Information technology advances to creates the structure of molecules, molecular modeling and ...calculate the binding energies of the drug to initiate a new medicine against neurodegenerative diseases. In our work, we implemented virtual screening of a drug-protein interaction is selected from drug data bank with potential drug bank inhibitory activity for a specific neurodegenerative disease. Here we analyze technical CADD studies of the neurodegenerative diseases. Finally selecting the best alkaloid for a specific neurodegenerative disease and predicting the efficiency using computation of alkaloid with molecular energy.
The evolutionary analysis of the genome of the immediate cluster is an important part of comparative genomics research. Identifying the overlap between immediate homologous clusters allows us to ...elucidate the function and evolution of proteins between species. Here, we report a network platform called Ortho-paralogous Venn-diagram representation that can be used to compare and visualize a wide range of ortho-paralogous clustering of genomes. In our work Ortho-paralogous Venn-diagram results show a functional summary of interactive Venn diagrams, summary counts, and interspecies shared cluster separations and intersections. Ortho-paralogous Venn-diagram also uses a variety of sequence analysis tools to gain an in-depth understanding of the cluster. In addition, Ortho-paralogous Venn identifies direct homologous clusters of single copy genes and allows custom search of specific gene clusters. It enables us in wide analysis of the genes and protein by comparing the genes using Venn diagram .Here the user can upload our own gene sequences into the application ,using three clustering approach to check the best clustering approches like SOM,K-means and advanced clustering after that we are using the Venn diagram repersentator to evolutionary cluster the genes having similar functionality and structural similarity from the uploaded data.Here we are using a venn diagram representation as an application which used to cluster the orthologous and paralogous gene on basics of their evolution and functional aspects.it enables us in wide analysis of the genes and protein bycomparing the genes using venn diagram representation.here the user can upload our own gene sequences into the application where the venn diagram representatorclusters.the genes having similar functionality and structural similarity from the uploaded data.
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