task_categories:image-classification annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual language:en license:unknown size_categories:1K<n<10K region:us legal signatures CEDAR
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- image-classification
pretty_name: Individuality Of Handwriting (CEDAR)
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': original
          '1': forgeries
  - name: individual
    dtype: uint8
  - name: figure
    dtype: uint8
  splits:
  - name: train
    num_bytes: 195780898.8
    num_examples: 2640
  download_size: 252337526
  dataset_size: 195780898.8
tags:
- legal
- signatures
- CEDAR
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Individuality Of Handwriting (CEDAR)

## Dataset Description

- **Homepage**: https://pubmed.ncbi.nlm.nih.gov/12136998/
- **Homepage**: https://cedar.buffalo.edu/NIJ/projectinfo.html

## Abstract

Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual.
Handwriting samples of 1,500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained.
Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting.
Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc.
These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches.
Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established.
The work is a step towards providing scientific support for admitting handwriting evidence in court.
The mathematical approach and the resulting software also have the promise of aiding the FDE.

Srihari SN, Cha SH, Arora H, Lee S. Individuality of handwriting. J Forensic Sci. 2002 Jul;47(4):856-72. PMID: 12136998.
