Using Convolutional Neural Networks for Content Extraction from Online Flyers
Authors: 
Alessandro Calefati, Ignazio Gallo, Alessandro Zamberletti, Lucia NoceAuthors Info & Claims
Alessandro Calefati, Ignazio Gallo, Alessandro Zamberletti, Lucia NoceAuthors Info & ClaimsPages 127 - 130
Abstract
The rise of online shopping has hurt physical retailers, which struggle to persuade customers to buy products in physical stores rather than online. Marketing flyers are a great mean to increase the visibility of physical retailers, but the unstructured offers appearing in those documents cannot be easily compared with similar online deals, making it hard for a customer to understand whether it is more convenient to order a product online or to buy it from the physical shop. In this work we tackle this problem, introducing a content extraction algorithm that automatically extracts structured data from flyers. Unlike competing approaches that mainly focus on textual content or simply analyze font type, color and text positioning, we propose a new approach that uses Convolutional Neural Networks to classify words extracted from flyers typically used in marketing materials to attract the attention of readers towards specific deals. We obtained good results and a high language and genre independence.
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Index Terms
- Using Convolutional Neural Networks for Content Extraction from Online Flyers
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Published: 13 September 2016
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DocEng '16 Paper Acceptance Rate 11 of 35 submissions, 31%;
Overall Acceptance Rate 194 of 564 submissions, 34%
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