We discuss a numerical algorithm for approximating the dynamics of moving curves and surfaces, called the Level-Set Method. This method allows us to write a program to predict the evolution of a propagating surface when the forces of propagation are known. Using the level-set method, we then create an easily coded numerical model of a disclination loop in the nematic liquid crystal 8CB (4, 4′-n-octylcynobiphenyl), a classic example of curvature-driven motion of a curve. We then discuss the first-order implementation of this numerical model and the various ways it may be improved.

전자장시스템에서 Level Set Method

Ancient history relies on disciplines such as epigraphy-the study of inscribed texts known as inscriptions-for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian's workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history. 

/pdf/restoring-and-attributing-ancient-texts-using-deep-neural-2ms89orm.pdf

Restoring and attributing ancient texts using deep neural networks

Recognition of Devanagari ancient handwritten character is an important task for resourceful contents' exploitation of the priceless information contained in them. There are numerous Devanagari ancient handwritten documents from fifteenth to the nineteenth century. This paper presents an optical character recognition system for the recognition of Devanagari ancient manuscripts. In this paper, improved recognition results for Devanagari ancient characters have been presented using the scale-invariant feature transform (SIFT) and Gabor filter feature extraction techniques.
Support vector machine (SVM) classifier is used for the classification task in this work. For experimental results, a database consisting of 5484 samples of Devanagari characters was collected from various ancient manuscripts placed in libraries and museums. SIFT- and Gabor filter-based features are used to extract the properties of the handwritten Devanagari ancient characters for recognition. Principle component analysis is used to reduce the length of the feature vector for reducing training time of the model and to improve recognition accuracy. Recognition accuracy of 91.39% has been achieved using the proposed system based on tenfold cross-validation technique and poly-SVM classifier.

https://link.springer.com/content/pdf/10.1007/s00500-020-05018-z.pdf

On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features

Devanagari script is the most widely used script in India and other Asian countries. There is a rich collection of ancient Devanagari manuscripts, which is a wealth of knowledge. To make these manuscripts available to people, efforts are being done to digitize these documents. Optical Character Recognition (OCR) plays an important role in recognizing these documents. Convolutional Neural Network (CNN) is a powerful model that is giving very promising results in the field of character recognition, pattern recognition etc. CNN has never been used for the recognition of the Devanagari ancient manuscripts. Our aim in the proposed work is to use the power of CNN for extracting the wealth of knowledge from Devanagari handwritten ancient manuscripts. In addition, we aim is to experiment with various design options like number of layes, stride size, number of filters, kenel size and different functions in various layers and to select the best of these. In this paper, the authors have proposed to use deep learning model as a feature extractor as well as a classifier for the recognition of 33 classes of basic characters of Devanagari ancient manuscripts. A dataset containing 5484 characters has been used for the experimental work. Various experiments show that the accuracy achieved using CNN as a feature extractor is better than other state-of-the-art techniques. The recognition accuracy of 93.73% has been achieved by using the model proposed in this paper for Devanagari ancient character recognition.

DeepNetDevanagari: a deep learning model for Devanagari ancient character recognition

Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers

Optical character recognition (OCR) is an important research area in the field of pattern recognition. A lot of research has been done on OCR in the last 60 years. There is a large volume of paper-based data in various libraries and offices. Also, there is a wealth of knowledge in the form of ancient text documents. It is a challenge to maintain and search from this paper-based data. At many places, efforts are being done to digitize this data. Paper based documents are scanned to digitize data but scanned data is in pictorial form. It cannot be recognized by computers because computers can understand standard alphanumeric characters as ASCII or some other codes. Therefore, alphanumeric information must be retrieved from scanned images. Optical character recognition system allows us to convert a document into electronic text, which can be used for edit, search, etc. operations. OCR system is the machine replication of human reading and has been the subject of intensive research for more than six decades. This paper presents a comprehensive survey of the work done in the various phases of an OCR with special focus on the OCR for ancient text documents. This paper will help the novice researchers by providing a comprehensive study of the various phases, namely, segmentation, feature extraction and classification techniques required for an OCR system especially for ancient documents. It has been observed that there is a limited work is done for the recognition of ancient documents especially for Devanagari script. This article also presents future directions for the upcoming researchers in the field of ancient text recognition.

Ancient text recognition: a review

Devanagari ancient document recognition process is drawing a lot of consideration from researchers nowadays. These ancient documents contain a wealth of knowledge. However, these documents are not available to all because of their fragile condition. A Devanagari ancient manuscript recognition system is designed for digital archiving. This system includes image binarization, character segmentation and recognition phases. It incorporates automatic recognition of scanned and segmented characters. Segmented characters may include basic characters (vowels and consonants), modifiers (matras) and various compound characters (characters formed by joining more than one basic characters). In this paper, handwritten Devanagari ancient manuscripts recognition system has been presented using statistical features extraction techniques. In feature extraction phase, intersection points, open endpoints, centroid, horizontal peak extent and vertical peak extent features are extracted. For classification, Convolutional Neural Network, Neural Network, Multilayer Perceptron, RBF-SVM and random forest techniques are considered in this work. Various feature extraction and classification techniques are considered and compared to the recognition of basic characters segmented from Devanagari ancient manuscripts. A data set, of 6152 pre-segmented samples of Devanagari ancient documents, is considered for experimental work. Authors have achieved 88.95% recognition accuracy using a combination of all features and a combination of all classifiers considered in this work by a simple majority voting scheme.

/pdf/devanagari-ancient-documents-recognition-using-statistical-2e1aso7lpj.pdf

Devanagari ancient documents recognition using statistical feature extraction techniques

Devanagari ancient manuscript recognition framework is drawing a lot of considerations from researchers nowadays. Devanagari ancient manuscripts are rare and delicate documents. To exploit the priceless information included in these documents, these documents are being digitized. Optical character recognition process is being used for the recognition of these documents. This paper presents a system for improvement in recognition of Devanagari ancient manuscripts using AdaBoost and Bagging methodologies. Discrete cosine transform (DCT) zigzag is used for feature extraction. Decision tree, Naive Bayes and support vector machine classifiers are used for the recognition of basic characters segmented from Devanagari ancient manuscripts. A dataset of 5484 pre-segmented characters of Devanagari ancient documents is considered for experimental work. Maximum recognition accuracy of 90.70% has been achieved using DCT zigzag features and RBF-SVM classifier. AdaBoost and Bagging ensemble methods are used with the base classifiers to improve the accuracy. Maximum accuracy of 91.70% is achieved for adaptive boosting (AdaBoost) with RBF-SVM. Various parameters for performance measures such as precision, recall, F-measure, false acceptance rate, false rejection rate and RMSE are used for assessing the quality of the ensemble methods.

Sonika Rani Narang

Papers

On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features

DeepNetDevanagari: a deep learning model for Devanagari ancient character recognition

Ancient text recognition: a review

Devanagari ancient documents recognition using statistical feature extraction techniques

Devanagari ancient character recognition using DCT features with adaptive boosting and bootstrap aggregating