Text-Independent Writer Identification and Verification Using Textural and Allographic Features

Abstract

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates.

Automatic writer identification framework for online handwritten documents using character prototypes

“…Table 2 provides a brief summary of some of the recent works in writer identification for the past decade. Interestingly, we observe a trend of more and more IR-based approaches [9,11,17,26] being proposed for writer identification in recent years. This gain in popularity is proof of the potential that such IR models can achieve for writer identification, in spite of the simplicity in its design.…”

“…Writer recognition systems can typically make use of global features such as texture, curvature and slant features [10][11][12] as well as a combination of local features such as graphemes, allographs and connected components [1,13,14] Our work falls into the latter category of text-independent techniques where the writers are not bounded by any specific lines of text in order for the system to recognize them. Instead, the system analyzes their handwriting styles through a series of automated processes, regardless of what they have written.…”

“…However, working at the grapheme level does not ensure a high level of consistency in reproducing the set of templates for writer identification. Another work that involves graphemes is by Bulacu et al [11]. They have generated a codebook of graphemes and combined them with features at the texture level to attain an identification rate of 92% on a data set of 650 writers.…”

Towards task-independent person authentication using eye movement signals

“…We call such problem as task-independent eye-movement biometrics. We stem this terminology from other behavioral biometric tasks, such as text-independent speaker [Kinnunen and Li 2010] and writer [Bulacu and Schomaker 2007] identification.…”

Fuzzy-Membership Based Writer Identification from Handwritten Devnagari Script

“…The text dependent method [7][8][9][10] is based on the writing style of character/word/lines. The text independent method [11][12][13][14] does not depend upon handwriting style.…”