The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Peter Z Revesz - One of the best experts on this subject based on the ideXlab platform.
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A method of identifying allographs in undeciphered Scripts and its application to the Indus Valley Script
Humanities and Social Sciences Communications, 2021Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:This work describes a general method of testing for redundancies in the sign lists of Ancient Scripts by data mining the positions of the signs within the inscriptions. The redundant signs are allographs of the same grapheme. The method is applied to the undeciphered Indus Valley Script, which stands out from other Ancient Scripts by having a large proposed sign list that contains dozens of asymmetric signs that have mirrored pairs. By a statistical analysis of mirrored asymmetric signs, this paper shows that the Indus Valley Script was multi-directional and the mirroring of signs often denotes only the direction of writing without any difference in meaning. For this and five other specific reasons listed in the paper, 50 pairs of signs, 23 mirrored, and 27 non-mirrored, can be grouped together because each pair consists of only insignificant variations of the same original sign. The reduced sign list may make decipherment easier in the future.
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data mining Ancient Scripts to investigate their relationships and origins
International Database Engineering and Applications Symposium, 2019Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:This paper describes a data mining study of a set of Ancient Scripts in order to discover their relationships, including their possible common origin from a single root script. The data mining uses convolutional neural networks and support vector machines to find the degree of visual similarity between pairs of symbols in eight different Ancient Scripts. Among the surprising results of the data mining are the following: (1) the Indus Valley Script is visually closest to Sumerian pictographs, and (2) the Linear B script is visually closest to the Cretan Hieroglyphic script.
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IDEAS - Data mining Ancient Scripts to investigate their relationships and origins
Proceedings of the 23rd International Database Applications & Engineering Symposium on - IDEAS '19, 2019Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:This paper describes a data mining study of a set of Ancient Scripts in order to discover their relationships, including their possible common origin from a single root script. The data mining uses convolutional neural networks and support vector machines to find the degree of visual similarity between pairs of symbols in eight different Ancient Scripts. Among the surprising results of the data mining are the following: (1) the Indus Valley Script is visually closest to Sumerian pictographs, and (2) the Linear B script is visually closest to the Cretan Hieroglyphic script.
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IDEAS - Data Mining Ancient Script Image Data Using Convolutional Neural Networks
Proceedings of the 22nd International Database Engineering & Applications Symposium on - IDEAS 2018, 2018Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:The recent surge in Ancient Scripts has resulted in huge image libraries of Ancient texts. Data mining of the collected images enables the study of the evolution of these Ancient Scripts. In particular, the origin of the Indus Valley script is highly debated. We use convolutional neural networks to test which Phoenician alphabet letters and Brahmi symbols are closest to the Indus Valley script symbols. Surprisingly, our analysis shows that overall the Phoenician alphabet is much closer than the Brahmi script to the Indus Valley script symbols.
Shruti Daggumati - One of the best experts on this subject based on the ideXlab platform.
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A method of identifying allographs in undeciphered Scripts and its application to the Indus Valley Script
Humanities and Social Sciences Communications, 2021Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:This work describes a general method of testing for redundancies in the sign lists of Ancient Scripts by data mining the positions of the signs within the inscriptions. The redundant signs are allographs of the same grapheme. The method is applied to the undeciphered Indus Valley Script, which stands out from other Ancient Scripts by having a large proposed sign list that contains dozens of asymmetric signs that have mirrored pairs. By a statistical analysis of mirrored asymmetric signs, this paper shows that the Indus Valley Script was multi-directional and the mirroring of signs often denotes only the direction of writing without any difference in meaning. For this and five other specific reasons listed in the paper, 50 pairs of signs, 23 mirrored, and 27 non-mirrored, can be grouped together because each pair consists of only insignificant variations of the same original sign. The reduced sign list may make decipherment easier in the future.
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data mining Ancient Scripts to investigate their relationships and origins
International Database Engineering and Applications Symposium, 2019Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:This paper describes a data mining study of a set of Ancient Scripts in order to discover their relationships, including their possible common origin from a single root script. The data mining uses convolutional neural networks and support vector machines to find the degree of visual similarity between pairs of symbols in eight different Ancient Scripts. Among the surprising results of the data mining are the following: (1) the Indus Valley Script is visually closest to Sumerian pictographs, and (2) the Linear B script is visually closest to the Cretan Hieroglyphic script.
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IDEAS - Data mining Ancient Scripts to investigate their relationships and origins
Proceedings of the 23rd International Database Applications & Engineering Symposium on - IDEAS '19, 2019Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:This paper describes a data mining study of a set of Ancient Scripts in order to discover their relationships, including their possible common origin from a single root script. The data mining uses convolutional neural networks and support vector machines to find the degree of visual similarity between pairs of symbols in eight different Ancient Scripts. Among the surprising results of the data mining are the following: (1) the Indus Valley Script is visually closest to Sumerian pictographs, and (2) the Linear B script is visually closest to the Cretan Hieroglyphic script.
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ADBIS (Short Papers and Workshops) - Similarity Queries on Script Image Databases
Communications in Computer and Information Science, 2018Co-Authors: Shruti DaggumatiAbstract:For a long time, researchers and archaeologists have studied evolution and similarity among Ancient Scripts. The vast image libraries which have recently become available allow for data mining to enable the study of evolution of these Ancient Scripts. In particular, the origin of the Indus Valley script is highly debated and is considered an undeciphered script. In this paper, we use convolutional neural networks to test which alphabets/symbols from various languages may be related to the Indus Valley script. The languages focused on include the Proto-Elamite script and Sumerian.
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IDEAS - Data Mining Ancient Script Image Data Using Convolutional Neural Networks
Proceedings of the 22nd International Database Engineering & Applications Symposium on - IDEAS 2018, 2018Co-Authors: Shruti Daggumati, Peter Z ReveszAbstract:The recent surge in Ancient Scripts has resulted in huge image libraries of Ancient texts. Data mining of the collected images enables the study of the evolution of these Ancient Scripts. In particular, the origin of the Indus Valley script is highly debated. We use convolutional neural networks to test which Phoenician alphabet letters and Brahmi symbols are closest to the Indus Valley script symbols. Surprisingly, our analysis shows that overall the Phoenician alphabet is much closer than the Brahmi script to the Indus Valley script symbols.
John Plaice - One of the best experts on this subject based on the ideXlab platform.
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A multidimensional approach to typesetting
2003Co-Authors: John Plaice, Yannis Haralambous, Paul Swoboda, C. A. RowleyAbstract:We propose to create a new model for multilingual computerized typesetting, in which each of language, script, font and character is treated as a multidimen- sional entity, and all combine to form a multidimensional context. Typesetting is undertaken in a typographical space, and becomes a multiple-stage process of preparing the input stream for typesetting, segmenting the stream into clusters or words, typesetting these clusters, and then recombining them. Each of the stages, including their respective algorithms, is dependent on the multidimensional context. This approach will support quality typesetting for a number of modern and Ancient Scripts. The paper and talk will show how these are to be implemented in .
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An extensible approach to high-quality multilingual typesetting
Proceedings. Seventeenth Workshop on Parallel and Distributed Simulation, 2003Co-Authors: John Plaice, Yannis Haralambous, Craig RowleyAbstract:We propose to create and study a new model for the micro-typography part of automated multilingual typesetting. This new model will support quality typesetting for a number of modern and Ancient Scripts. The major innovations in the proposal are: the process is refined into four phases, each dependent on a multidimensional tree-structured context summarizing the current linguistic and cultural environment. The four phases are: preparing the input stream for typesetting; segmenting the stream into clusters (words); typesetting these clusters; and then recombining the clusters into a typeset text stream. The context is pervasive throughout the process; the algorithms used in each phase are context-dependent, as are the meanings of fundamental entities such as language, script, font and character.
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RIDE - An extensible approach to high-quality multilingual typesetting
Proceedings. Seventeenth Workshop on Parallel and Distributed Simulation, 1Co-Authors: John Plaice, Yannis Haralambous, C. A. RowleyAbstract:We propose to create and study a new model for the micro-typography part of automated multilingual typesetting. This new model will support quality typesetting for a number of modern and Ancient Scripts. The major innovations in the proposal are: the process is refined into four phases, each dependent on a multidimensional tree-structured context summarizing the current linguistic and cultural environment. The four phases are: preparing the input stream for typesetting; segmenting the stream into clusters (words); typesetting these clusters; and then recombining the clusters into a typeset text stream. The context is pervasive throughout the process; the algorithms used in each phase are context-dependent, as are the meanings of fundamental entities such as language, script, font and character.
C. A. Rowley - One of the best experts on this subject based on the ideXlab platform.
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A multidimensional approach to typesetting
2003Co-Authors: John Plaice, Yannis Haralambous, Paul Swoboda, C. A. RowleyAbstract:We propose to create a new model for multilingual computerized typesetting, in which each of language, script, font and character is treated as a multidimen- sional entity, and all combine to form a multidimensional context. Typesetting is undertaken in a typographical space, and becomes a multiple-stage process of preparing the input stream for typesetting, segmenting the stream into clusters or words, typesetting these clusters, and then recombining them. Each of the stages, including their respective algorithms, is dependent on the multidimensional context. This approach will support quality typesetting for a number of modern and Ancient Scripts. The paper and talk will show how these are to be implemented in .
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RIDE - An extensible approach to high-quality multilingual typesetting
Proceedings. Seventeenth Workshop on Parallel and Distributed Simulation, 1Co-Authors: John Plaice, Yannis Haralambous, C. A. RowleyAbstract:We propose to create and study a new model for the micro-typography part of automated multilingual typesetting. This new model will support quality typesetting for a number of modern and Ancient Scripts. The major innovations in the proposal are: the process is refined into four phases, each dependent on a multidimensional tree-structured context summarizing the current linguistic and cultural environment. The four phases are: preparing the input stream for typesetting; segmenting the stream into clusters (words); typesetting these clusters; and then recombining the clusters into a typeset text stream. The context is pervasive throughout the process; the algorithms used in each phase are context-dependent, as are the meanings of fundamental entities such as language, script, font and character.
Alejandre H. Toselli - One of the best experts on this subject based on the ideXlab platform.
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ICFHR - Active Learning in Handwritten Text Recognition using the Derivational Entropy
2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018Co-Authors: Verónica Romero, Joan Andreu Sanchez, Alejandre H. ToselliAbstract:Handwritten Text Recognition systems are based on statistical models such as recurrent neural networks or hidden Markov models for optical modeling of characters. These models need large corpora for training, consisting in text line images with their corresponding tranScripts. The manual annotation of this training data is expensive because it is carried out by experts in paleography, who are specialized in reading Ancient Scripts. An alternative to reduce the annotation human effort is to use Active Learning techniques to selecting the most informative samples to be used for training. In this paper we study an Active Learning technique to selecting the most informative samples in an HTR scenario. The expert paleographer transcribes only the most informative samples in each stage. The technique followed here is based in the derivational entropy computed from word-graphs obtained from the recognition process.
