Offline Text-Independent Writer Identification based on word level data
Vineet Kumar, Suresh Sundaram

TL;DR
This paper introduces a text-independent writer identification method using word-level handwritten images, combining SIFT, CNN, and entropy-based weighting to improve accuracy and computational efficiency.
Contribution
It presents a novel approach integrating SIFT, CNN, and entropy-based feature weighting for writer identification from word images, without size restrictions.
Findings
Effective on CVL and IAM databases
Outperforms previous methods in accuracy
Robust to varying word sizes
Abstract
This paper proposes a novel scheme to identify the authorship of a document based on handwritten input word images of an individual. Our approach is text-independent and does not place any restrictions on the size of the input word images under consideration. To begin with, we employ the SIFT algorithm to extract multiple key points at various levels of abstraction (comprising allograph, character, or combination of characters). These key points are then passed through a trained CNN network to generate feature maps corresponding to a convolution layer. However, owing to the scale corresponding to the SIFT key points, the size of a generated feature map may differ. As an alleviation to this issue, the histogram of gradients is applied on the feature map to produce a fixed representation. Typically, in a CNN, the number of filters of each convolution block increase depending on the depth…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Authorship Attribution and Profiling · Natural Language Processing Techniques
MethodsConvolution
