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Reading the legends of Roman Republican coins

Authors:

Albert Kavelar,

Sebastian Zambanini,

Martin KampelAuthors Info & Claims

Journal on Computing and Cultural Heritage (JOCCH), Volume 7, Issue 1

Article No.: 5, Pages 1 - 20

https://doi.org/10.1145/2583115

Published: 01 April 2014 Publication History

Abstract

Coin classification is one of the main aspects of numismatics. The introduction of an automated image-based coin classification system could assist numismatists in their everyday work and allow hobby numismatists to gain additional information on their coin collection by uploading images to a respective Web site. For Roman Republican coins, the inscription is one of the most significant features, and its recognition is an essential part in the successful research of an image-based coin recognition system. This article presents a novel way for the recognition of ancient Roman Republican coin legends. Traditional optical character recognition (OCR) strategies were designed for printed or handwritten texts and rely on binarization in the course of their recognition process. Since coin legends are simply embossed onto a piece of metal, they are of the same color as the background and binarization becomes error prone and prohibits the use of standard OCR. Therefore, the proposed method is based on state-of-the-art scene text recognition methods that are rooted in object recognition. Sift descriptors are computed for a dense grid of keypoints and are tested using support vector machines trained for each letter of the respective alphabet. Each descriptor receives a score for every letter, and the use of pictorial structures allows one to detect the optimal configuration for the lexicon words within an image; the word causing the lowest costs is recognized. Character and word recognition capabilities of the proposed method are evaluated individually; character recognition is benchmarked on three and word recognition on different datasets. Depending on the Sift configuration, lexicon, and dataset used, the word recognition rates range from 29% to 67%.

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Cited By

Anwar HAnwar SZambanini SPorikli F(2021)Deep ancient Roman Republican coin classification via feature fusion and attentionPattern Recognition10.1016/j.patcog.2021.107871114(107871)Online publication date: Jun-2021
https://doi.org/10.1016/j.patcog.2021.107871
Anwar HSabetghadam SBell P(2020)An Image-Based Class Retrieval System for Roman Republican CoinsEntropy10.3390/e2208079922:8(799)Online publication date: 22-Jul-2020
https://doi.org/10.3390/e22080799
Hess MMacDonald LValach J(2018)Application of multi-modal 2D and 3D imaging and analytical techniques to document and examine coins on the example of two Roman silver denariiHeritage Science10.1186/s40494-018-0169-26:1Online publication date: 8-Feb-2018
https://doi.org/10.1186/s40494-018-0169-2
Show More Cited By

Index Terms

Reading the legends of Roman Republican coins
1. Applied computing
  1. Arts and humanities
    1. Fine arts
    2. Performing arts
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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Reviews

Reviewer: Richard John Botting

If you Google "Republican Roman coins" you will be faced with thousands of fascinating images. There are three identifying features on most of them. Most show a head or object. Most have a mark identifying the mint. Most have a word known as the "legend." The legend might be "ROMA" or "III VIR," for example. This paper is about recognizing the legends found on Roman coins minted between 280 and 27 BC. These coins look old, and are often beaten up. Recognizing their legends defeats normal optical character recognition (OCR) techniques. This paper makes the case that reading legends on images of coins is almost as hard as decoding CAPTCHAs. This paper should be required reading for researchers and grad students in scene text recognition (STR). It includes an in-depth survey of the field and adds new ideas. At first glance, I thought the proposed architecture (Figure 3) revisited Sedgwick's Pandaemonium [1], but the solutions in this paper use state-of-the-art scale-invariant feature transforms (SIFTs) and support vector machines (SVMs). These are in turn guided by gathering features only in regions of interest (ROI). These are places in the image with large entropy. I would have preferred to see more detail about the features that were used, but this would make the paper too long. The resulting system achieves 25 to 65 percent recognition. We are still a long way from an app to help an amateur who stumbles across a coin in the back garden. Online Computing Reviews Service

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Published In

cover image Journal on Computing and Cultural Heritage

Journal on Computing and Cultural Heritage Volume 7, Issue 1

February 2014

99 pages

ISSN:1556-4673

EISSN:1556-4711

DOI:10.1145/2582016

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2014

Accepted: 01 October 2013

Revised: 01 July 2013

Received: 01 May 2013

Published in JOCCH Volume 7, Issue 1

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Cited By

Anwar HAnwar SZambanini SPorikli F(2021)Deep ancient Roman Republican coin classification via feature fusion and attentionPattern Recognition10.1016/j.patcog.2021.107871114(107871)Online publication date: Jun-2021
https://doi.org/10.1016/j.patcog.2021.107871
Anwar HSabetghadam SBell P(2020)An Image-Based Class Retrieval System for Roman Republican CoinsEntropy10.3390/e2208079922:8(799)Online publication date: 22-Jul-2020
https://doi.org/10.3390/e22080799
Hess MMacDonald LValach J(2018)Application of multi-modal 2D and 3D imaging and analytical techniques to document and examine coins on the example of two Roman silver denariiHeritage Science10.1186/s40494-018-0169-26:1Online publication date: 8-Feb-2018
https://doi.org/10.1186/s40494-018-0169-2
Anwar HZambanini SKampel MVondrovec K(2015)Ancient Coin Classification Using Reverse Motif Recognition: Image-based classification of Roman Republican coinsIEEE Signal Processing Magazine10.1109/MSP.2015.240933132:4(64-74)Online publication date: Jul-2015
https://doi.org/10.1109/MSP.2015.2409331
Zambanini SKavelar AKampel M(2014)Classifying Ancient Coins by Local Feature Matching and Pairwise Geometric Consistency EvaluationProceedings of the 2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.523(3032-3037)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1109/ICPR.2014.523

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