Writer Identification Using Inexpensive Signal Processing Techniques

TL;DR

This paper explores efficient, pattern recognition-based methods for quick writer identification from scanned handwritten documents using signal processing techniques within a modular framework.

Contribution

It introduces a novel approach combining classical and audio signal processing techniques for visual writer identification, emphasizing efficiency and preliminary accuracy.

Findings

Demonstrated the feasibility of inexpensive signal processing for writer identification

Implemented a comparative study of multiple algorithm combinations in Java

Presented preliminary experimental results on simulated visual identification

Abstract

We propose to use novel and classical audio and text signal-processing and otherwise techniques for "inexpensive" fast writer identification tasks of scanned hand-written documents "visually". The "inexpensive" refers to the efficiency of the identification process in terms of CPU cycles while preserving decent accuracy for preliminary identification. This is a comparative study of multiple algorithm combinations in a pattern recognition pipeline implemented in Java around an open-source Modular Audio Recognition Framework (MARF) that can do a lot more beyond audio. We present our preliminary experimental findings in such an identification task. We simulate "visual" identification by "looking" at the hand-written document as a whole rather than trying to extract fine-grained features out of it prior classification.