Frugal Handwritten Text Recognition for Sub-Saharan Civil-Status Registers: A Smartphone-to-Records Pipeline with CPU-Only Inference

Ousmane Hamadou, Jean Louis Kedieng Ebongue Fendji

Handwritten civil-status registers across Sub-Saharan Africa are being photographed, but rarely transcribed, which leaves their content unsearchable. Local administrations that hold these records typically lack GPUs, reliable connectivity, and the budget for external digitization services. We study whether a frugal pipeline, with consumer-grade capture and CPU-only inference, can make such registers machine-readable. We introduce a corpus of 1,568 smartphone images of handwritten birth, marriage, and death records from a municipal registry in Chad, with line-level transcriptions for 185 records. Our pipeline combines a fully classical preprocessing stage, running at about 2.6 images per second on a laptop CPU, with a Kraken recognizer fine-tuned from a pretrained model in about four hours on a free-tier GPU. On manually corrected line segmentation and a single-registry test set whose writers also appear in training, fine-tuning reduces the character error rate from 14.53% to 0.50% and the word error rate from 56.93% to 3.37%. On a six-core laptop CPU, the recognizer weighs 61 MiB, peaks below 2.6 GiB of RAM, and processes an image in under 6 s on average, excluding line…