Google patents text reader that understands layout without reading order

1. A processing system comprising: a memory storing a neural network comprising a graph convolutional network and a transformer; and one or more processors coupled to the memory and configured to classify text from a given document, comprising: generating a beta-skeleton graph based on a plurality of tokens, each given token of the plurality of tokens corresponding to a given string of text in the given document, and wherein the beta-skeleton graph comprises, for each given token: a node corresponding to the given token and comprising a vector based on content and location of the given string of text within the given document; one or more edges, each edge of the one or more edges linking the node corresponding to the given token to a neighboring node corresponding to another token of the plurality of tokens; and an edge embedding for each edge based on a spatial relationship comprising at least one of vertical distance, horizontal distance, or aspect ratio between the tokens; generating, using the graph convolutional network, a plurality of supertokens based on the beta-skeleton graph, each given supertoken of the plurality of supertokens being based at least in part on the vector of a given node and the vector of each neighboring node to which the given node is linked via one of its one or more edges, and further based on the edge embedding of each corresponding edge, wherein the plurality of supertokens preserves local syntactic information independently of document serialization; generating, using the transformer, a plurality of predictions based on the plurality of supertokens; and generating a set of classifications based on the plurality of predictions, the set of classifications identifying at least one entity class corresponding to at least one token of the plurality of tokens.