List and tabular data extraction system and method
AssigneeAccenture Global Solutions Limited
InventorsAndre Chatzistamatiou, Florin Cremenescu, Yizhen Dai, Ludo Gerardus Wilhelmus van Alst
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Abstract
A system and method for automating and improving tabular and list-based data extraction from a variety of document types is disclosed. The system and method detect and sort which documents include tables or lists, and performs row and column segmentation. In addition, the system and method apply Conditional Random Fields models to localize each table and semantic data understanding to map and export the extracted data to the desired format and arrangement.
Claims20
A computer-implemented method of tabular or list-based data extraction from document images, the method comprising:
- receiving, at a server and from a first data source, a first document including a first page;
- performing a column-wise pixel analysis of the first page, thereby determining that the first page includes a first table;
- performing column segmentation based on signal analysis of column-wise mean pixel values of the first page, thereby identifying a set of columns;
- performing row segmentation using optical character recognition (OCR)-generated bounding boxes, thereby identifying a set of rows;
- selecting which rows of the set of rows belong to the first table using a first Conditional Random Fields (CRF) model, thereby localizing the first table on the first page;
- selecting, for each column in the set of columns, a header name from a pre-defined set of header names, the selection being based on a classification performed by a second CRF model that evaluates at least the entire contents of that column;
- mapping each item of data extracted from a cell in the first table to a field using semantic data understanding;
- and
- generating a first digital table representing data extracted from the first table for presentation in a user interface.
A system for tabular or list-based data extraction from document images, the system comprising:
- a processor;
- machine-readable media including instructions which, when executed by the processor, cause the processor to:
- receive, at a server and from a first data source, a first document including a first page;
- perform a column-wise pixel analysis of the first page, thereby determining that the first page includes a first table;
- perform column segmentation based on signal analysis of column-wise mean pixel values of the first page, thereby identifying a set of columns;
- perform row segmentation using optical character recognition (OCR)-generated bounding boxes, thereby identifying a set of rows;
- select which rows of the set of rows belong to the first table using a first Conditional Random Fields (CRF) model, thereby localizing the first table on the first page;
- select, for each column in the set of columns, a header name from a pre-defined set of header names, the selection being based on a classification performed by a second CRF model that evaluates at least the entire contents of that column;
- map each item of data extracted from a cell in the first table to a field using semantic data understanding;
- and
- generate a first digital table representing data extracted from the first table for presentation in a user interface.
A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to:
- receive, at a server and from a first data source, a first document including a first page;
- perform a column-wise pixel analysis of the first page, thereby determining that the first page includes a first table;
- perform column segmentation based on signal analysis of column-wise mean pixel values of the first page, thereby identifying a set of columns;
- perform row segmentation using optical character recognition (OCR)-generated bounding boxes, thereby identifying a set of rows;
- select which rows of the set of rows belong to the first table using a first Conditional Random Fields (CRF) model, thereby localizing the first table on the first page;
- select, for each column in the set of columns, a header name from a pre-defined set of header names, the selection being based on a classification performed by a second CRF model that evaluates at least the entire contents of that column;
- map each item of data extracted from a cell in the first table to a field using semantic data understanding;
- and
- generate a first digital table representing data extracted from the first table for presentation in a user interface.
Dependencies are inferred from the claim text.