Historical sources written in rare or undeciphered scripts represent an immense but underexploited part of the world’s cultural and linguistic heritage. Their study is often hindered by fragmentary preservation, non-standard symbol systems, and the absence of interoperable digital resources. While recent advances in imaging, transcription, and computational analysis have improved access to historical texts, most tools rely on large quantities of labeled data and standardized encodings, requirements that are rarely met for rare or unknown writing systems. This paper presents the design and methodology of a new corpus and database dedicated to rare and undeciphered scripts worldwide. The resource integrates high-quality images, transliterations, transcriptions, linguistic annotations, and metadata within a unified data model tailored for low-resource and non-standard scripts. By adhering to FAIR principles and existing standards for linguistic and cultural heritage data, the database enables reproducible, interdisciplinary research across philology, linguistics, cryptology, and computer science. The paper outlines the data collection and digitization workflow, describes the metadata and database architecture, and demonstrates applications in analysis and decipherment.

We present the first ever dataset of manually segmented and transcribed Ajami manuscripts written in Wolof. The term Ajami refers to modified Arabic-script orthographies used to transcribe African languages. Handwritten text recognition (HTR) and optical character recognition (OCR) models for Arabic-script languages perform poorly on African languages written in Ajami orthographies because these languages are not represented in the pre-training data of the models. This leads to recognition models being unable to extract unique Arabic-script letters and ubiquitous diacritics used in African languages, and struggling to adapt to various calligraphy styles used across Africa. We release the following as an open-source dataset: an ALTO formatting of high-quality images of handwritten and printed, 20th–century Wolof manuscripts; manual segmentation (region and line); and manual transcriptions. We extend our contribution by evaluating several Arabic-script recognition models intended for historical manuscripts and find they produce character error rates (CER) of 61–81%. Transcriptions produced by the evaluated recognition models, as well as a keyboard to transcribe Wolof Ajami manuscripts, are released as well. The digitally transcribed text in the dataset can also be utilized for various natural language processing (NLP) and historical linguistic tasks.