TSCC

The Indus script is one of the major undeciphered scripts of the ancient world. The small size of the corpus, the absence of bilingual texts, and the lack of definite knowledge of the underlying language has frustrated efforts at decipherment since the discovery of the remains of the Indus civilization. Building on previous statistical approaches, we apply the tools of statistical language processing, specifically n-gram Markov chains, to analyze the syntax of the Indus script. We find that unigrams follow a Zipf-Mandelbrot distribution. Text beginner and ender distributions are unequal, providing internal evidence for syntax. We see clear evidence of strong bigram correlations and extract significant pairs and triplets using a log-likelihood measure of association. Highly frequent pairs and triplets are not always highly significant. The model performance is evaluated using information-theoretic measures and cross-validation. The model can restore doubtfully read texts with an accuracy of about 75%. We find that a quadrigram Markov chain saturates information theoretic measures against a held-out corpus. Our work forms the basis for the development of a stochastic grammar which may be used to explore the syntax of the Indus script in greater detail.

Archaeological excavations in the sites of the Indus Valley civilization (2500-1900 BCE) in Pakistan and northwestern India have unearthed a large number of artifacts with inscriptions made up of hundreds of distinct signs. To date, there is no generally accepted decipherment of these sign sequences, and there have been suggestions that the signs could be non-linguistic. Here we apply complex network analysis techniques on the database of available Indus inscriptions, with the aim of detecting patterns indicative of syntactic structure in this sign system. Our results show the presence of regularities, e.g., in the segmentation trees of the sequences, that suggest the existence of a grammar underlying the construction of the sequences.

A diffusion equation has been used to study migration of early humans into the South Asian subcontinent. The diffusion equation is tempered by a set of parameters that account for geographical features like proximity to water resources, altitude, and flatness of land. The ensuing diffusion of populations is followed in time-dependent computer simulations carried out over a period of 10,000 YBP. The geographical parameters are determined from readily-available satellite data. The results of our computer simulations are compared to recent genetic data so as to better correlate the migratory patterns of various populations; they suggest that the initial populations started to coalesce around 4,000 YBP before the commencement of a period of relative geographical isolation of each population group. The period during which coalescence of populations occurred appears consistent with the established timeline associated with the Harappan civilization and also, with genetic admixing that rece...

One of the most important undeciphered scripts of the ancient world is the Indus script. Earlier studies had focused on the correlations between signs in the Indus texts using various statistical and computational techniques such as N-grams or Markov chains. In the present study, K-means clustering, an unsupervised machine learning technique is used to identify clusters of similar texts without making any assumptions about its content. The technique is effective in extracting significant clusters and patterns in the script. Nine clusters are extracted from this study. The texts in each cluster share a common set of structural elements and are more similar to each other than the texts in other clusters. The clusters, as extracted from the study, reveal inherent patterns due to adjacent and non-adjacent dependencies between signs in the Indus texts. These clusters have definitive patterns in the usage of the signs but are only weakly associated to any archaeological site or medium of writing. The characteristic signature features of each cluster are identified in the study. The study provides a good handle to extract the logic of writing in the Indus script.

The script of the Indus valley civilization has defied decipherment. Several attempts have been made in the past to decipher the script but there is no consensus about its content. The lack of definite knowledge about its structure makes it difficult to objectively evaluate any claim of decipherment. We have tried to fill this lacuna by analyzing the structure of the script using various computational techniques including machine learning and data mining. The focus of our study is to identify patterns in the Indus writing and explore its underlying logic without making any assumptions about its content. The methods identified in the study can also be used to analyse the structure of other undeciphered scripts. In the present paper we summarize our studies of the Indus script.