FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation

• URL: http://arxiv.org/abs/2005.14229v1
• Date: Thu, 28 May 2020 18:47:10 GMT
• Title: FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation
• Authors: Celso A. M. Lopes Junior, Matheus Henrique M. da Silva, Byron Leite Dantas Bezerra, Bruno Jose Torres Fernandes, and Donato Impedovo
• Abstract summary: We propose an approach to locate and extract the pixels of handwritten signatures on identification documents. The technique is based on a fully convolutional encoder-decoder network combined with a block of refinement layers for the alpha channel of the predicted image.
• Score: 3.3144312096837325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although secular, handwritten signature is one of the most reliable biometric methods used by most countries. In the last ten years, the application of technology for verification of handwritten signatures has evolved strongly, including forensic aspects. Some factors, such as the complexity of the background and the small size of the region of interest - signature pixels - increase the difficulty of the targeting task. Other factors that make it challenging are the various variations present in handwritten signatures such as location, type of ink, color and type of pen, and the type of stroke. In this work, we propose an approach to locate and extract the pixels of handwritten signatures on identification documents, without any prior information on the location of the signatures. The technique used is based on a fully convolutional encoder-decoder network combined with a block of refinement layers for the alpha channel of the predicted image. The experimental results demonstrate that the technique outputs a clean signature with higher fidelity in the lines than the traditional approaches and preservation of the pertinent characteristics to the signer's spelling. To evaluate the quality of our proposal, we use the following image similarity metrics: SSIM, SIFT, and Dice Coefficient. The qualitative and quantitative results show a significant improvement in comparison with the baseline system.

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