Evaluating Shape Descriptors for Detection of Maya Hieroglyphs

Abstract. In this work we address the problem of detecting instances of complex shapes in binary images. We investigated the effects of com-bining DoG and Harris-Laplace interest points with SIFT and HOOSC descriptors. Also, we propose the use of a retrieval-based detection frame-work suitable to deal with images that are sparsely annotated, and where the objects of interest are very small in proportion to the total size of the image. Our initial results suggest that corner structures are suitable points to compute local descriptors for binary images, although there is the need for better methods to estimate their appropriate characteristic scale when used on binary images

The Mesoamerican Corpus of Formative Period Art and Writing

This project explores the origins and development of the first writing in the New World by constructing a comprehensive database of Formative period, 1500-400 BCE, iconography and a suite of database-driven digital tools. In collaboration with two of the largest repositories of Formative period Mesoamerican art in Mexico, the project integrates the work of archaeologists, art historians, and scientific computing specialists to plan and begin the production of a database, digital assets, and visual search software that permit the visualization of spatial, chronological, and contextual relationships among iconographic and archaeological datasets. These resources will eventually support mobile and web based applications that allow for the search, comparison, and analysis of a corpus of material currently only partially documented. The start-up phase will generate a functional prototype database, project website, wireframe user interfaces, and a report summarizing project development

Visual Analysis of Maya Glyphs via Crowdsourcing and Deep Learning

In this dissertation, we study visual analysis methods for complex ancient Maya writings. The unit sign of a Maya text is called glyph, and may have either semantic or syllabic significance. There are over 800 identified glyph categories, and over 1400 variations across these categories. To enable fast manipulation of data by scholars in Humanities, it is desirable to have automatic visual analysis tools such as glyph categorization, localization, and visualization. Analysis and recognition of glyphs are challenging problems. The same patterns may be observed in different signs but with different compositions. The inter-class variance can thus be significantly low. On the opposite, the intra-class variance can be high, as the visual variants within the same semantic category may differ to a large extent except for some patterns specific to the category. Another related challenge of Maya writings is the lack of a large dataset to study the glyph patterns. Consequently, we study local shape representations, both knowledge-driven and data-driven, over a set of frequent syllabic glyphs as well as other binary shapes, i.e. sketches. This comparative study indicates that a large data corpus and a deep network architecture are needed to learn data-driven representations that can capture the complex compositions of local patterns. To build a large glyph dataset in a short period of time, we study a crowdsourcing approach as an alternative to time-consuming data preparation of experts. Specifically, we work on individual glyph segmentation out of glyph-blocks from the three remaining codices (i.e. folded bark pages painted with a brush). With gradual steps in our crowdsourcing approach, we observe that providing supervision and careful task design are key aspects for non-experts to generate high-quality annotations. This way, we obtain a large dataset (over 9000) of individual Maya glyphs. We analyze this crowdsourced glyph dataset with both knowledge-driven and data-driven visual representations. First, we evaluate two competitive knowledge-driven representations, namely Histogram of Oriented Shape Context and Histogram of Oriented Gradients. Secondly, thanks to the large size of the crowdsourced dataset, we study visual representation learning with deep Convolutional Neural Networks. We adopt three data-driven approaches: assess- ing representations from pretrained networks, fine-tuning the last convolutional block of a pretrained network, and training a network from scratch. Finally, we investigate different glyph visualization tasks based on the studied representations. First, we explore the visual structure of several glyph corpora by applying a non-linear dimensionality reduction method, namely t-distributed Stochastic Neighborhood Embedding, Secondly, we propose a way to inspect the discriminative parts of individual glyphs according to the trained deep networks. For this purpose, we use the Gradient-weighted Class Activation Mapping method and highlight the network activations as a heatmap visualization over an input image. We assess whether the highlighted parts correspond to distinguishing parts of glyphs in a perceptual crowdsourcing study. Overall, this thesis presents a promising crowdsourcing approach, competitive data-driven visual representations, and interpretable visualization methods that can be applied to explore various other Digital Humanities datasets

Maya Codical Glyph Segmentation: A Crowdsourcing Approach

This paper focuses on the crowd-annotation of an ancient Maya glyph dataset derived from the three ancient codices that survived up to date. More precisely, non-expert annotators are asked to segment glyph-blocks into their constituent glyph entities. As a means of supervision, available glyph variants are provided to the annotators during the crowdsourcing task. Compared to object recognition in natural images or handwriting transcription tasks, designing an engaging task and dealing with crowd behavior is challenging in our case. This challenge originates from the inherent complexity of Maya writing and an incomplete understanding of the signs and semantics in the existing catalogs. We elaborate on the evolution of the crowdsourcing task design, and discuss the choices for providing supervision during the task. We analyze the distributions of similarity and task difficulty scores, and the segmentation performance of the crowd. A unique dataset of over 9000 Maya glyphs from 291 categories individually segmented from the three codices was created and will be made publicly available thanks to this process. This dataset lends itself to automatic glyph classification tasks. We provide baseline methods for glyph classification using traditional shape descriptors and convolutional neural networks

Pengenalan Karakter Hieroglif Mesir Kuno Menggunakan Convolutional Neural Network

This research implements a Convolutional Neural Network (CNN) to recognize ancient Egyptian hieroglyphics. CNN is a deep learning architecture that automatically learns the features of data hierarchically. The CNN technique effectively integrates feature extraction and classifiers into one system. This study used hieroglyphic characters from the pyramid of Unas, which consisted of 170 kinds of characters, but this study only used 11 kinds of characters that had a sample size above 100, namely characters D21, E34, G17, G43, I9, M17, N35, O50, S29, V31, and X1. The results showed that the accuracy achieved was 99%. This research is expected to help archaeologists, enthusiasts, tourists, and museum visitors to recognize hieroglyphic characters as historical objects that only a few people know. Keywords: character recognition, ancient Egyptian hieroglyphics, convolutional neural networkPenelitian ini mengimplementasikan Convolutional Neural Network (CNN) untuk mengenali Hieroglif Mesir kuno. CNN adalah salah satu arsitektur deep learning yang secara otomatis mempelajari fitur pada sebuah data secara hierarki. CNN secara efektif mengintegrasikan ekstraksi fitur dan pengklasifikasi ke dalam satu sistem. Penelitian ini menggunakan karakter hieroglif dari piramida Unas yang terdiri dari 170 jenis karakter, namun penelitian ini hanya menggunakan 11 jenis karakter yang memiliki jumlah sampel di atas 100 yaitu karakter D21, E34, G17, G43, I9, M17, N35, O50, S29, V31, dan X1. Hasil penelitian menunjukkan bahwa akurasi yang diperoleh mencapai 99%. Penelitian ini diharapkan dapat membantu arkeolog, peminat, turis, dan pengunjung museum untuk mengenali karakter atau tulisan hieroglif sebagai salah satu benda bersejarah yang hanya diketahui oleh beberapa orang saja. Kata Kunci: pengenalan karakter, hieroglif Mesir kuno, convolutional neural networ

A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval

We introduce a shape descriptor that extracts keypoints from binary images and automatically detects the salient ones among them. The proposed descriptor operates as follows: First, the contours of the image are detected and an image transformation is used to generate background information. Next, pixels of the transformed image that have specific characteristics in their local areas are used to extract keypoints. Afterwards, the most salient keypoints are automatically detected by filtering out redundant and sensitive ones. Finally, a feature vector is calculated for each keypoint by using the distribution of contour points in its local area. The proposed descriptor is evaluated using public datasets of silhouette images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned logos. Experimental results show that the proposed descriptor compares strongly against state of the art methods, and that it is reliable when applied on challenging images such as fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descripto

Writing as Material Practice: Substance, Surface and Medium

Writing as Material Practice grapples with the issue of writing as a form of material culture in its ancient and more recent manifestations, and in the contexts of production and consumption. Fifteen case studies explore the artefactual nature of writing — the ways in which materials, techniques, colour, scale, orientation and visibility inform the creation of inscribed objects and spaces, as well as structure subsequent engagement, perception and meaning making. Covering a temporal span of some 5000 years, from c.3200 BCE to the present day, and ranging in spatial context from the Americas to the Near East, the chapters in this volume bring a variety of perspectives which contribute to both specific and broader questions of writing materialities. The authors also aim to place past graphical systems in their social contexts so they can be understood in relation to the people who created and attributed meaning to writing and associated symbolic modes through a diverse array of individual and wider social practices

A REFLECTION OF MAYA REPRESENTATION, DISTRIBUTION, AND INTERACTION: CERAMIC FIGURINES FROM THE LATE CLASSIC SITE OF CANCUÉN, PETÉN DEPARTMENT, GUATEMALA

This project explores intersecting spheres of technological, stylistic and contextually patterned relationships expressed by ceramic figurines associated with the major Maya polity Cancuén. Cancuén is situated by assessing its external contacts by reference to figurines recovered from several Late Classic settlements, and hieroglyphic texts recorded as interacting polities. By focusing on these sites along connecting waterways, I attempt to discern directions of influence and change with regard to figurine use patterns relative to those seen in other ceramic representations. Traditional archaeological criteria were used to obtain excavated figurines at specific sites. Stylistic and technological information are augmented through an intensive use of chemical data obtained by neutron activation analysis (INAA). The exploration of these diverse data sets permits aspects of material culture from the site of Cancuén to assist in determining varying expressions of social interactions, and economic boundaries, as reflected in the ceramic figurines

Ancient Households of the Americas

Several different cultures — Iroquois, Coosa, Anasazi, Hohokam, San Agustín, Wankarani, Formative Gulf Coast Mexico, and Formative, Classic, Colonial, and contemporary Maya — are analyzed through the lens of household archaeology in concrete, data-driven case studies. "This excellent book should be heavily used by anyone with an interest in household archaeology." —North American Archaeologist "There are a number of excellent studies that scholars interested in household archaeology will find highly useful." —Journal of Anthropological Research "This collection underscores the importance of household archaeology to the study of social dynamics." —Choice "This volume is an impressive one. . . . In an era in which household archaeology has become essential to archaeological praxis, this volume is indeed essential reading." —Cambridge Archaeological Journa

Ancient Households of the Americas

Several different cultures — Iroquois, Coosa, Anasazi, Hohokam, San Agustín, Wankarani, Formative Gulf Coast Mexico, and Formative, Classic, Colonial, and contemporary Maya — are analyzed through the lens of household archaeology in concrete, data-driven case studies. "This excellent book should be heavily used by anyone with an interest in household archaeology." —North American Archaeologist "There are a number of excellent studies that scholars interested in household archaeology will find highly useful." —Journal of Anthropological Research "This collection underscores the importance of household archaeology to the study of social dynamics." —Choice "This volume is an impressive one. . . . In an era in which household archaeology has become essential to archaeological praxis, this volume is indeed essential reading." —Cambridge Archaeological Journa