Z-Transform Exponential Approximation of One-Dimensional Functions: Theory and Applications

Balashkov, M. V.; Bogachev, Vyacheslav M.

doi:10.3103/s0735272721040038

Cited by 1 publication

(2 citation statements)

References 15 publications

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“…Moreover, pre-selection methods also remove a huge number of irrelevant target words which have no similarity to the query word. Applying affine and 2D Z-transform [32,33] to all target words would greatly increase the complexity of the proposal and it is reduced using the proposed pre-selection method. After that, we convert the profile extracted previously from the query word and the pre-selected target words into Bezier curves.…”

Section: Present Methodsmentioning

confidence: 99%

“…The next requirement of the work is an efficient profile matching technique which is the major objective since failure in fulfilling this objective means the selection of a large number of probable candidate query word images as a keyword. In this work, for the first time, we have applied the 2D Z-transform-based [31][32][33] string matching technique that calculates the similarity between two profiles (upper or lower) extracted from a query and a word from the probable candidate query word image set. The notable problem that is faced by the 2D Z-transform-based profile matching scheme is that it would return same word written differently as different word.…”

Section: Objectives and Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

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

Banerjee

Bhowal²,

Malakar³

et al. 2022

Int J Comput Intell Syst

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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.

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Section: Present Methodsmentioning

confidence: 99%

Section: Objectives and Contributionsmentioning

confidence: 99%