# Offline Text-Independent Writer Identification based on word level data

arXiv: https://arxiv.org/abs/2202.10207 · HTML: https://mlsanity.com/paper/2202.10207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 66 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks](https://mlsanity.com/paper/1406.6909/) (2014)
- [Unsupervised Feature Learning for Writer Identification and Writer Retrieval](https://mlsanity.com/paper/1705.09369/) (2017)
- [FragNet: Writer Identification using Deep Fragment Networks](https://mlsanity.com/paper/2003.07212/) (2020)
- [DeepWriterID: An End-to-end Online Text-independent Writer Identification System](https://mlsanity.com/paper/1508.04945/) (2015)
- [DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification](https://mlsanity.com/paper/1606.06472/) (2016)
- [GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification](https://mlsanity.com/paper/2104.05036/) (2021)
- [Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images](https://mlsanity.com/paper/1809.10954/) (2018)
- [Text-independent writer identification using convolutional neural network](https://mlsanity.com/paper/2009.04877/) (2020)

## Built on by (most cited first, 1 of 1)

- [Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification](https://mlsanity.com/paper/2510.18671/) (2025)

## Abstract (arXiv)

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 of the network. Thus, extracting histogram features for each of the convolution feature map increase the dimension as well as the computational load. To address this aspect, we use an entropy-based method to learn the weights of the feature maps of a particular CNN layer during the training phase of our algorithm. The efficacy of our proposed system has been demonstrated on two publicly available databases namely CVL and IAM. We empirically show that the results obtained are promising when compared with previous works.