Imagine discovering an ancient civilization with a written language no one can read—a script full of symbols and secrets, etched onto seals, pottery, and stone, yet to reveal its story. That’s the mystery of the Indus Valley civilization, one of the oldest and most advanced societies of its time, but one whose writing, the Indus script, remains undeciphered.

For decades, archaeologists and linguists have tried to crack the code, but the lack of a “Rosetta Stone” and the scarcity of inscriptions have kept the secret hidden. Now, in the age of AI, a new breed of researchers is turning to technology to do what centuries of human effort have struggled to achieve: using machine learning to decode the Indus script.

In this article, we’ll dive into the exciting world of AI and ancient scripts, focusing on a groundbreaking project led by researchers at Tokyo Metropolitan University. Their work in creating a unique dataset of Indus signs is giving machine learning the fuel it needs to potentially solve this ancient puzzle.

The Indus Script: A Code Waiting to Be Cracked

Over 4,000 inscriptions of the Indus script have been found on artifacts such as seals, copper plates, and pottery, dating back to 2600 BCE. The symbols in these inscriptions, known as Indus signs, are believed to represent a sophisticated language. But with no bilingual texts (think of how the Rosetta Stone helped decode Egyptian hieroglyphs), researchers have had little to go on.

The sheer challenge of deciphering the Indus script has always been linked to the limited data available. Most of the inscriptions are short, offering too few clues for traditional linguistic analysis. But modern times call for modern solutions—and that’s where machine learning and big data come into play.

The Handwritten Indus Signs Dataset: Giving AI a Fighting Chance

One of the latest breakthroughs in the field comes from the work of Sujata Saini and her colleagues at Tokyo Metropolitan University. Faced with the challenge of limited original inscriptions, the team took an innovative approach: why not have people help create a dataset?

In their research, participants were asked to hand-draw 10 of the most frequently used Indus signs using a custom-built web application. Imagine the process: you look at an ancient symbol on your screen and try to replicate it with your mouse or pen tablet. The result? A collection of 1,000 unique handwritten images, reflecting the diversity of human strokes and styles—just like the variations you'd find in ancient carvings.

This dataset is now fueling the training of convolutional neural networks (CNNs), a type of deep learning model that excels at recognizing patterns in images. The results? A model that can classify Indus signs with an impressive 93% accuracy! This success suggests that AI could finally be our key to unlocking the language of the ancient Indus civilization.

Why This Matters: The Power of Human-AI Collaboration

What’s truly exciting about this project is that it’s not just about machines solving ancient mysteries. It’s a collaboration between human creativity and cutting-edge AI technology. The dataset created by asking participants to draw Indus signs by hand provides a richness and variety that traditional data sources simply couldn’t offer. By leveraging the unique ways people interpret and replicate these ancient symbols, the dataset introduces the kind of diversity that can make AI models smarter and more robust.

It’s not just about finding patterns in uniform data—it’s about training AI to deal with the messiness of real-world variations, which is exactly what we see in the ancient inscriptions. Some signs might be carved deep into stone, others lightly scratched onto pottery. Some may be partially damaged or worn away with time. By feeding the AI a dataset full of diverse, human-drawn signs, we’re teaching it to recognize and classify even the most challenging examples.

What’s Next? A Future of Bigger Datasets and Deeper Insights

This is only the beginning. While the current dataset has proven incredibly useful, the road ahead is paved with even more ambitious plans. The next step is to expand the dataset to include more Indus signs and variations, which will allow researchers to refine their models and push the accuracy even higher. By building larger, richer datasets, the team aims to tackle more complex aspects of the script, bringing us ever closer to cracking the code.

As the project grows, so does the excitement. The fusion of human effort and machine learning is opening new doors in the world of ancient languages and beyond. Imagine what we could learn—not just about the Indus Valley civilization, but about other ancient cultures whose writing has yet to be deciphered. The possibilities are endless.

Conclusion: The Future of Indus Script Research

The journey to decode the Indus script has taken a massive leap forward, thanks to the innovative use of AI and collaborative data collection. While we’re still far from fully understanding this ancient language, projects like this are paving the way for breakthroughs that once seemed impossible.

And the best part? This is just the beginning. Our future research will include a larger and more diverse dataset, enabling even deeper insights that will bring us closer to finally understanding the language of the Indus Valley civilization. We are also going to propose two new big datasets created with AI soon, which will further accelerate our progress in unraveling this ancient script. The mystery remains—for now—but with AI on our side, we might just be on the brink of one of the greatest discoveries in human history. Stay tuned!

References

• Saini, S., Shibata, H., & Takama, Y. (2022). Toward Construction of Handwritten Indus Signs Dataset. Presented at the 10th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2022), Session No. C2-2, Beijing, China. Available at https://www.isciia2022.org/.
• Saini, S., Shibata, H., & Takama, Y. (2024). Construction of Handwritten Indus Signs Dataset Employing Social Approach. Published in the Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 28, No. 1. DOI: https://doi.org/10.20965/jaciii.2024.p0122.

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