# Offline Writer Identification Using Convolutional Neural Network Activation Features Source: [Offline Writer Identification Using Convolutional Neural Network Activation Features]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:24:11.561Z Authors: Vincent Christlein, David Bernecker, Andreas Maier, Elli Angelopoulou Published: 2024-02-26T21:16:14.000Z ## Abstract Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of mAP on the challenging bilingual ICDAR dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()