2026-10-13T18:30:54Z https://data.mendeley.com/oai

oai:data.mendeley.com/cz8xsdyt22.1 2026-07-17T14:04:34Z

K P, Dinesh A Multi-Session Smartphone-Scanned Handwriting Corpus for Open-Set Writer Identification Mendeley Data Smartphone-scanned handwritten laboratory records from a first-year engineering Makerspace course: 65 writers x 17 lab experiments, 565 PDF records, 2,463 pages at 300 DPI. 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. NAMING AND ANONYMISATION. Every record is student<NNN>.pdf, where NNN is a 3-digit pseudonym assigned per writer; original upload filenames are never included and no mapping to real identities is published. Valid ids: student002–student136, even numbers only. Experiment identity is defined by the folder name. Exactly one PDF per student per experiment; byte-identical re-uploads were removed (MD5-verified) and duplicate scans resolved by page count. KNOWN ARTIFACTS (documented and deliberately retained): mirrored show-through and unmirrored stack-transparency ghosts from thin double-sided paper, ruled lines, uncontrolled lighting and perspective. ETHICS AND USAGE. Handwriting is a biometric. Released for non-commercial research in document analysis and writer identification (CC BY-NC 4.0). Do not attempt to re-identify writers; do not use this data to train handwriting-forgery systems targeting real individuals. Computer Vision Biometrics Handwriting Dataset doi:10.17632/cz8xsdyt22.1 oai:data.mendeley.com/cz8xsdyt22.1 Attribution Noncommercial 4.0 International https://creativecommons.org/licenses/by-nc/4.0/ https://data.mendeley.com/datasets/cz8xsdyt22 2026-07-17T14:04:34Z