Z-Transform-Based Profile Matching to Develop a Learning-Free Keyword Spotting Method for Handwritten Document Images

Abstract

For easy accessibility of the information from the digitized document images, optical character recognition (OCR)-based software can be used. But in the case of handwritten documents, the performance of the state-of-the-art OCR systems is not satisfactory owing to the complexity of the unconstrained handwriting. Hence, research affinity comes up with an alternative solution for this problem called keyword spotting (KWS) which is much more practical than an OCR-based solution. This work proposes a novel learning-free KWS method that can be applied to a heterogeneous collection of handwritten documents. In this work, we introduce a new way of profile matching to compare the query word profiles (i.e., both upper and lower) with the target words’ profiles. At first, both query and target words are binarized, and then two profiles from each such word are generated. Next, we formulate rules to filter out the irrelevant words concerning the query word and obtain the probable candidate query (i.e., target) words. Then we compare the profiles of the query and candidate query words in the Z-transform domain using the condition of resonance for the damped oscillator. However, before the match, we perform an affine transformation on the Bezier curve representation of the profiles of the candidate query words to reduce the effects like scaling, rotation, and shearing which might occur due to the variant writing styles of individuals. The proposed method achieves satisfactory performance compared to state-of-the-art learning-free methods when applied to four publicly available standard datasets namely ICFHR 2014 H-KWS competition Modern, IAM, ICFHR 2016 H-KWS competition Botany and ICFHR 2016 H-KWS competition Konzilsprotokolle datasets.