In particular, LoRRA <cite>[44]</cite> extends previous VQA models [43] with an OCR attention branch and adds OCR tokens as a dynamic vocabulary to the answer classifier, allowing copying a single OCR token from the image as the answer. Similarly in [37] , OCR tokens are grouped into blocks and added to the output space of a VQA model. While these approaches enable reading text in images to some extent, they typically rely on custom pairwise multimodal fusion mechanisms between two modalities (such as single-hop attention over image regions and text tokens, conditioned on the input question), which limit the types of possible interactions between modalities. Furthermore, they treat answer prediction as a single-step classification problem -either selecting an answer from the training set answers or copying a text token from the image -making it difficult to generate complex answers such as book titles or signboard names with multiple words, or answers with both common words and specific image text tokens, such as McDonald's burger where McDonald's is from text in the image and burger is from the model's own vocabulary. In addition, the word embedding based image text features in previous work have limited representation power and miss important cues such as the appearance (e.g. font and color) and the location of text tokens in images. For example, tokens that have different fonts and are spatially apart from each other usually do not belong to the same street sign. In this paper, we address the above limitations with our novel Multimodal Multi-Copy Mesh (M4C) model for the TextVQA task, based on the transformer [48] architecture accompanied by iterative answer decoding through dynamic pointers, as shown in Figure 1 .