MATrIX -- Modality-Aware Transformer for Information eXtraction

We present MATrIX - a Modality-Aware Transformer for Information eXtraction in the Visual Document Understanding (VDU) domain. VDU covers information extraction from visually rich documents such as forms, invoices, receipts, tables, graphs, presentations, or advertisements. In these, text semantics and visual information supplement each other to provide a global understanding of the document. MATrIX is pre-trained in an unsupervised way with specifically designed tasks that require the use of multi-modal information (spatial, visual, or textual). We consider the spatial and text modalities all at once in a single token set. To make the attention more flexible, we use a learned modality-aware relative bias in the attention mechanism to modulate the attention between the tokens of different modalities. We evaluate MATrIX on 3 different datasets each with strong baselines.

A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition

Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions using features extracted with a multi-scale convolutional neural network (CNN) fed into a bidirectional long short term memory (LSTM) network. This framework achieves comparable error rates to state of the art frameworks while using less memory and time. The results in this paper demonstrate the potential of this framework and future work can investigate production ready and deployable handwritten text recognisers.