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arXiv Computer Vision By Seyed Mohammad Abuzar Hashemi, Mihir Chauhan, Jun Chu, Sargur Srihari

Hybrid Feature Learning for Handwriting Verification

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The paper introduces a Hybrid Deep Learning (HDL) architecture that combines Auto-Learned Features (ALF) and Human-Engineered Features (HEF) for handwriting verification. ALF is extracted using a Two Channel Convolutional Neural Network (TC-CNN) or a Two Channel Autoencoder (TC-AE), while HEF is obtained via Gradient Structural Concavity (GSC) or Scale Invariant Feature Transform (SIFT). Experiments on 150,000 pairs of the word "AND" from 1,500 writers show that the HDL model using AE-GSC achieves 99.7% accuracy on a seen writer dataset and 92.16% on a shuffled writer dataset, outperforming CEDAR-FOX, and AE-SIFT performs comparably on unseen writers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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