A Multi-Session Smartphone-Scanned Handwriting Corpus for Open-Set Writer Identification

Every page was captured by the students themselves with a mobile-phone camera through consumer scanning apps (Adobe Scan, CamScanner; app watermarks appear on some pages) — no flatbed scanner anywhere in the corpus. The data is multi-session (each experiment written and scanned on a different day), diagram-rich (inline circuit and mechanical sketches, pencil shading), double-sided cursive Indian-college English — a capture condition and demographic absent from existing writer-identification benchmarks such as IAM, CVL, CERUG and Firemaker.

CONTENTS. corpus/ms_<experiment>.zip — 17 zips, one per lab experiment, each containing student<NNN>.pdf records. tables/dataset_long.csv — one row per PDF (student_id, experiment, filename, page count). tables/dataset_matrix.csv — student x experiment page-count matrix. tables/dataset_summary.json — corpus totals and processing flags. tables/splits.json — the canonical writer-disjoint train/validation/test split (45/10/10) for reproducible benchmarking. checksums_sha256.txt — SHA-256 of every file.