Seeing Through Time: Can AI Decode Ancient Roman Coin Imagery?

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Author: Denis Avetisyan

A new study investigates whether modern computer vision techniques, specifically Vision Transformers and Convolutional Neural Networks, can accurately identify motifs on ancient Roman coins.

The convolutional neural network, when identifying key features, demonstrated a pronounced focus on the upper-left quadrant of images, frequently misinterpreting depictions of crosses carried by angels - often found on coins bearing shield imagery on their reverse - as shields themselves.
The convolutional neural network, when identifying key features, demonstrated a pronounced focus on the upper-left quadrant of images, frequently misinterpreting depictions of crosses carried by angels – often found on coins bearing shield imagery on their reverse – as shields themselves.

Researchers compare the performance of Vision Transformers and Convolutional Neural Networks on a semantic image analysis task using a dataset of ancient Roman coinage.

Analyzing the vast and fragmented corpus of ancient coinage presents a significant challenge for extracting meaningful historical insights. This paper, ‘Do Transformers Understand Ancient Roman Coin Motifs Better than CNNs?’, investigates the efficacy of deep learning architectures-specifically Vision Transformers (ViT) and Convolutional Neural Networks (CNNs)-for the automated identification of semantic elements depicted on ancient Roman coins. Results demonstrate that ViT models achieve comparable, and in some cases superior, accuracy to newly trained CNNs in this complex image analysis task. Could this shift in methodology unlock previously inaccessible details within numismatic collections and reshape our understanding of ancient Roman history and iconography?

Unveiling History: The Challenge of Ancient Numismatic Analysis

The field of ancient numismatics, the study of coins, has long depended on the meticulous observation and expert judgment of scholars. Determining a coin’s origin, date, and historical significance requires careful analysis of minute details – subtle variations in iconography, lettering, and metal composition. Traditionally, this process has been intensely manual, relying on a specialist’s trained eye to discern patterns and anomalies within a collection. This expertise encompasses not only recognizing established types but also interpreting the effects of centuries of wear, corrosion, and damage, all of which can obscure crucial identifying features. The depth of knowledge required makes large-scale cataloging and research exceptionally time-consuming, creating a significant bottleneck for accessing and understanding the historical information embedded within these small, yet remarkably resilient, artifacts.

Traditional computer vision techniques often falter when applied to ancient coins due to the significant impact of time and handling. Simple image matching algorithms, for example, rely on pixel-by-pixel comparisons, but centuries of wear, corrosion, and even intentional defacement drastically alter a coin’s surface. These variations extend beyond mere discoloration; details can be worn smooth, edges chipped, and metallic composition altered, creating images that differ substantially from pristine examples. Consequently, algorithms trained on ideal coin images struggle to accurately identify or classify coins exhibiting these common forms of degradation, hindering efforts to automate the analysis of large numismatic collections and demanding more robust and adaptable approaches to image recognition.

The inability of current computer vision systems to effectively categorize ancient coins presents a significant obstacle to numismatic research. Large coin collections, often numbering in the tens or even hundreds of thousands of items, remain largely unstudied due to the sheer volume of manual work required for identification and classification. This bottleneck prevents scholars from conducting comprehensive analyses of trade routes, economic trends, and cultural exchange throughout history. Automated systems, if successful, would unlock these vast datasets, facilitating large-scale research previously considered impossible and potentially revealing new insights into ancient civilizations. The challenge isn’t simply recognizing an image, but understanding how wear, corrosion, and striking variations affect the appearance of a coin while still accurately identifying its origin and historical context.

Ancient coin imagery presents a unique challenge to computer vision systems due to its intricate details and the substantial effects of time. Simple feature extraction methods, designed to identify basic shapes or colors, often fail when confronted with the nuanced designs, corrosion, wear, and striking variations inherent in these historical artifacts. The very nature of coin engraving – utilizing relief, complex iconography, and often minuscule lettering – necessitates techniques that go beyond superficial analysis. Successful automated classification and study require algorithms capable of discerning subtle patterns, compensating for image degradation, and understanding the contextual significance of design elements – a level of sophistication demanding advanced approaches like deep learning and convolutional neural networks to effectively ‘read’ and interpret these miniature works of art.

This image, containing multiple coins, demonstrates a failure of the preprocessing stage which should have flagged it for rejection.
This image, containing multiple coins, demonstrates a failure of the preprocessing stage which should have flagged it for rejection.

Semantic Understanding: A New Vision for Image Analysis

Semantic Content Analysis represents a shift in image understanding from identifying patterns in pixel values to interpreting the objects and relationships within an image. Traditional pixel-level matching assesses similarity based on raw data, proving ineffective when variations in lighting, perspective, or minor occlusions occur. In contrast, semantic analysis aims to extract high-level features representing the ‘meaning’ of image regions – for example, identifying a ‘car’ or a ‘person’ – allowing for more robust image retrieval and classification. This approach enables systems to recognize objects even under significant visual changes and facilitates tasks requiring an understanding of scene context, such as object detection and image captioning.

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) currently define the leading edge of deep learning approaches to image analysis. CNNs excel at identifying spatial hierarchies within images through the application of convolutional filters, effectively learning features like edges, textures, and shapes. ViTs, conversely, apply the transformer architecture – initially developed for natural language processing – to image data by dividing images into patches and treating them as sequences. This allows the model to capture long-range dependencies and global context more effectively than traditional CNNs. Both architectures utilize multiple layers to progressively extract more abstract and complex features, culminating in a representation suitable for tasks such as image classification, object detection, and image segmentation. Recent advancements often combine elements of both CNNs and ViTs to leverage their respective strengths.

Achieving optimal performance with deep learning models for semantic understanding, specifically Convolutional Neural Networks (CNNs) and Vision Transformers, necessitates substantial computational resources. Training these models often requires high-performance computing infrastructure, including GPUs or TPUs, due to the large number of parameters and the extensive datasets involved. Furthermore, careful training procedures are crucial, encompassing techniques such as data augmentation, regularization, and hyperparameter tuning. Insufficient computational power or inadequate training methodologies can lead to underfitting, overfitting, or slow convergence, ultimately limiting the model’s ability to generalize to unseen data and accurately interpret visual content. The complexity of these models also demands significant engineering effort to optimize for efficient memory usage and parallel processing.

Robust data preprocessing is critical for achieving high accuracy and generalization in deep learning models for image analysis. This process typically involves several steps, including data cleaning to handle missing or erroneous values, normalization to scale pixel intensities to a standard range-often between 0 and 1-and data augmentation techniques such as rotations, flips, and crops to artificially increase the size and diversity of the training dataset. Furthermore, data splitting into distinct training, validation, and testing sets is essential for model evaluation and preventing overfitting. Consistent application of these preprocessing steps ensures that the model learns meaningful features rather than spurious correlations present in the raw data, ultimately improving its ability to perform accurately on unseen images.

The ViT model identified a more diverse and distributed set of salient regions in images of horses, indicating it learned a broader range of visual features compared to the CNN model.
The ViT model identified a more diverse and distributed set of salient regions in images of horses, indicating it learned a broader range of visual features compared to the CNN model.

Optimizing Model Training for Ancient Coin Classification

Effective model training for ancient coin classification utilizes optimization algorithms to minimize the loss function during iterative adjustments to model parameters. Stochastic Gradient Descent (SGD) updates parameters based on the gradient calculated from a single training example or a small batch, offering computational efficiency but potentially noisy convergence. Adam Optimizer, an adaptive learning rate method, combines the benefits of both AdaGrad and RMSProp, adjusting the learning rate for each parameter individually based on estimates of the first and second moments of the gradients. This often results in faster and more stable convergence compared to traditional SGD, particularly in high-dimensional parameter spaces. The selection of an appropriate optimization algorithm, and tuning of its hyperparameters such as learning rate and momentum, is critical for achieving optimal model performance and generalization capability.

Loss functions are a critical component of model training, serving to quantify the discrepancy between a model’s predicted classification and the ground truth label. Specifically, Cross-Entropy Loss is frequently employed in multi-class classification problems, measuring the difference between the predicted probability distribution and the actual distribution – where the correct class has a probability of 1 and all others are 0. The resulting scalar value represents the error, and the training process aims to minimize this loss through iterative adjustments to the model’s parameters. A lower loss value indicates a better alignment between predictions and actual classifications, effectively guiding the model towards improved accuracy. The mathematical formulation of Cross-Entropy Loss for a single example is Loss = - \sum_{i=1}^{C} y_i \log(\hat{y}_i) , where C is the number of classes, y_i is the true label (0 or 1), and \hat{y}_i is the predicted probability for class i .

ReLU, or Rectified Linear Unit, activation functions improve model performance in ancient coin classification by introducing non-linearity to the model. Linear models are limited in their ability to represent complex relationships within image data; ReLU addresses this by outputting the input directly if it is positive, or zero otherwise f(x) = max(0, x) . This non-linear transformation allows the model to learn more intricate patterns and decision boundaries, improving accuracy in distinguishing between different coin types, wear levels, and potential forgeries. Without such non-linear activations, deep neural networks would essentially behave as a single linear transformation, significantly reducing their representational capacity.

Saliency maps are visualizations that highlight the image regions most influential in a convolutional neural network’s classification decision. These maps are generated by calculating the gradient of the output class score with respect to the input image pixels; larger gradients indicate greater influence. By visualizing these gradients as a heatmap overlaid on the original image, researchers can assess whether the model is focusing on relevant features – such as diagnostic markings, wear patterns, or metallic composition – or spurious correlations. This interpretability allows for targeted refinement of the training dataset, model architecture, or hyperparameters to improve classification accuracy and robustness, particularly when dealing with the complex and often degraded imagery of ancient coins.

Training performance initially improves but eventually plateaus and declines due to overfitting, indicating the model has begun to memorize the training data rather than generalize from it.
Training performance initially improves but eventually plateaus and declines due to overfitting, indicating the model has begun to memorize the training data rather than generalize from it.

Beyond Classification: Unlocking New Historical Insights

Automated coin identification and dating, once a laborious task for numismatists, is now achievable through the synergy of computer vision and machine learning. These technologies enable systems to ‘see’ and interpret coin imagery, extracting crucial features like inscriptions, portraits, and iconography. Machine learning algorithms then utilize these features to classify coins, determining their origin, emperor, and approximate date of minting. This process moves beyond simple visual comparison, allowing for the analysis of large datasets and the identification of subtle variations that might escape human observation. The resulting automated systems not only accelerate research but also facilitate the preservation of cultural heritage by creating comprehensive and searchable digital collections of ancient coinage.

Enhancing the precision of automated coin identification relies heavily on sophisticated feature extraction methods. Recent advancements demonstrate that techniques like Compact Bilinear Pooling (CBP) and Directional Kernel Features significantly outperform traditional approaches. CBP efficiently captures complex relationships between image features, creating a more robust representation of the coin’s visual characteristics, while Directional Kernel Features excel at identifying edges and textures regardless of their orientation – crucial for deciphering worn or partially obscured details. These methods effectively address challenges posed by variations in image quality, lighting conditions, and coin wear, leading to demonstrably improved classification accuracy and a more reliable means of analyzing numismatic artifacts. The integration of these techniques allows for a finer-grained analysis, revealing subtle visual cues previously lost in conventional image processing pipelines.

Recognizing ancient coins presents a unique challenge due to the frequent variations in their orientation during image capture. Researchers addressed this issue by incorporating Rotation Transformation Networks into their image analysis pipeline. These networks effectively learn to correct for rotational differences, allowing the identification model to focus on the coin’s intrinsic features rather than its angle. By augmenting the input images with learned rotational corrections, the system demonstrates increased robustness and significantly improves classification accuracy, even when coins are presented at arbitrary orientations. This approach bypasses the need for extensive data augmentation with rotated images, streamlining the training process and reducing computational demands while enhancing overall performance in real-world scenarios.

A novel approach to coin identification utilizes Graph Transduction Games, a semi-supervised learning technique that effectively combines the strengths of both labeled and unlabeled data. This method frames the learning process as a game played on a graph representing the coin collection, where nodes are individual coins and edges signify similarities. By propagating information across this graph, the system can infer labels for previously unseen coins – those without explicit dating or identification – based on the characteristics of their labeled neighbors. This is particularly valuable when dealing with ancient numismatic data, where obtaining complete and accurate labels can be time-consuming and expensive. The technique demonstrates an ability to improve classification performance by leveraging the inherent structure within the coin collection, ultimately reducing the reliance on large, fully labeled datasets and offering a pathway toward more efficient and accurate automated analysis.

Recent research indicates that Vision Transformer (ViT) models are proving remarkably adept at analyzing ancient coinage, achieving recognition accuracy for semantic elements – such as portraits, inscriptions, and symbols – that is statistically comparable to that of traditional Convolutional Neural Networks (CNNs). This finding is significant because ViTs, originally developed for natural language processing, represent a fundamentally different approach to image analysis, relying on self-attention mechanisms to identify relationships between image patches. The study demonstrates that ViTs can achieve performance within a few percentage points of established CNN architectures, suggesting a viable alternative for automated coin analysis and opening avenues for exploring the strengths of transformer-based models in the field of archaeological image processing. This parity in performance, despite architectural differences, highlights the potential for transfer learning and cross-disciplinary innovation in computer vision.

Each data sample pairs images of a coin’s obverse and reverse sides with a corresponding textual description.
Each data sample pairs images of a coin’s obverse and reverse sides with a corresponding textual description.

The study’s exploration of Vision Transformers alongside Convolutional Neural Networks highlights a pursuit of elegant solutions to complex problems. It’s not merely about achieving comparable performance – though the results demonstrate ViT’s efficacy in semantic image analysis of ancient coin motifs – but about refining the architecture to better suit the nuances of the data. As Yann LeCun once stated, “Deep learning is about finding the right representations.” This sentiment resonates deeply with the paper’s core concept; the researchers aren’t simply applying a model, they are investigating whether a different representational approach – ViT’s attention mechanism – can unlock a more harmonious understanding of ancient numismatic data, where every detail, however small, potentially holds historical significance. The interface, in this case the model’s ability to ‘see’ and interpret, sings when these elements harmonize.

Beyond the Denarius: Charting a Course Forward

The demonstrated parity between Vision Transformers and Convolutional Neural Networks in discerning ancient Roman coin motifs offers a subtle, yet important, lesson. It is not merely about achieving higher accuracy – a metric often pursued with zealous, almost frantic, energy – but about recognizing where the limitations truly reside. The current work, while competent, skirts the periphery of genuine semantic understanding. The models identify what is depicted – an emperor’s bust, a military standard – but remain largely oblivious to why. The subtle interplay of iconography, political messaging, and historical context remains stubbornly opaque.

Future effort should not concentrate solely on architectural refinements, or the pursuit of ever-larger datasets. Instead, a more elegant approach necessitates a move toward models capable of reasoning about the depicted scenes. Incorporating knowledge graphs, drawing upon established numismatic scholarship, and developing methods for representing historical context represent more promising avenues. A truly insightful system would not simply identify a “dolphin” on a coin, but infer its association with a particular emperor, a specific naval victory, or a prevailing cultural symbol.

Ultimately, the task transcends computer vision. It demands a synthesis of artificial intelligence, historical analysis, and a humble acknowledgment that pattern recognition, however sophisticated, is but a single step toward genuine comprehension. The pursuit of such understanding, though challenging, is where the true reward lies – a reward measured not in percentage points, but in a deeper appreciation of the past.

Original article: https://arxiv.org/pdf/2601.09433.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-15 07:19