Abstract: Method includes extracting, from document image, tables as objects; outputting tables in an XML format; filtering tables based on keywords and a condition including one from among AND and OR, which includes: for each of tables, determining whether the table includes at least one from among a first keyword and a second keyword, for condition being AND, based on the table containing first keyword and second keyword, outputting the table, as filtered table, and, for condition being OR, based on the table containing first keyword or second keyword, outputting the table, as filtered table, thereby outputting filtered tables that include the first keyword and/or the second keyword based on condition; executing query on filtered tables that includes keys composed based on cell level information associated with filtered tables; identifying cells based on keys; and providing target content that corresponds to cells, as response to query.

Abstract: Method includes obtaining historical document images including text that correspond to different document classes; and generating a dictionary using text of the historical document images. The dictionary includes base words occurring with a greatest frequency in each document class. The base words are extracted from the text of the historical document images and arranged in datasets by a document class, where each dataset includes the base words of a same document class that occur with the greatest frequency within that document class. Trie structure is generated using the base words of the datasets that occur with a greatest frequency in each dataset. The trie structure includes internal nodes including root node and leaf nodes in which keys corresponding to the base words occurring with the greatest frequency in each dataset are respectively stored in predefined order. The trie structure is searchable in the predefined order starting with the root node.

Abstract: Aspects of the present disclosure are directed to extracting textual information from image documents. In one embodiment, a system, upon receiving a request to extract textual information from an image document, a digital processing system performs character recognition based on content of the image document using multiple approaches to generate corresponding texts. The texts are then combined to determine a result text representing the textual information contained in the image document. The result is then provided as a response to the request.

Abstract: Method includes generating a base model by training a pretrained model using a base training dataset including first training datapoints identifying tables in historical document images that include the tables and text, where the generated base model is configured to extract the tables as objects; and generating a table extraction model by training the base model using an enhanced training dataset including second training datapoints that are different from the first training datapoints and identify a plurality of cells disposed in each of the tables in a row direction and a column direction. The table extraction model is trained to output content of the tables and table information in an XML format, the table information including cell level information of the plurality of cells that is searchable via a query configured to provide target content that corresponds to one or more cells.