  Tables on the web constitute a valuable data source for many applications,
like factual search and knowledge base augmentation. However, as genuine tables
containing relational knowledge only account for a small proportion of tables
on the web, reliable genuine web table classification is a crucial first step
of table extraction. Previous works usually rely on explicit feature
construction from the HTML code. In contrast, we propose an approach for web
table classification by exploiting the full visual appearance of a table, which
works purely by applying a convolutional neural network on the rendered image
of the web table. Since these visual features can be extracted automatically,
our approach circumvents the need for explicit feature construction. A new hand
labeled gold standard dataset containing HTML source code and images for 13,112
tables was generated for this task. Transfer learning techniques are applied to
well known VGG16 and ResNet50 architectures. The evaluation of CNN image
classification with fine tuned ResNet50 (F1 93.29%) shows that this approach
achieves results comparable to previous solutions using explicitly defined HTML
code based features. By combining visual and explicit features, an F-measure of
93.70% can be achieved by Random Forest classification, which beats current
state of the art methods.
