Abstract: According to one embodiment, a computer-implemented method for detecting and classifying columns of tables and/or tabular data arrangements within image data includes: detecting one or more tables and/or one or more tabular data arrangements within the image data; extracting the one or more tables and/or the one or more tabular data arrangements from the processed image data; and classifying either: a plurality of columns of the one or more extracted tables; a plurality of columns of the one or more extracted tabular data arrangements; or both the columns of the one or more extracted tables and the columns of the one or more extracted tabular data arrangements. Corresponding systems and computer program products are also disclosed.

Abstract: According to one embodiment, a computer-implemented method for detecting and classifying columns of tables and/or tabular data arrangements within image data includes: detecting one or more tables and/or one or more tabular data arrangements within the image data; extracting the one or more tables and/or the one or more tabular data arrangements from the processed image data; and classifying either: a plurality of columns of the one or more extracted tables; a plurality of columns of the one or more extracted tabular data arrangements; or both the columns of the one or more extracted tables and the columns of the one or more extracted tabular data arrangements. Corresponding systems and computer program products are also disclosed.

Abstract: Recent developments in machine learning (commonly coined “artificial intelligence” or “AI”) have vastly expanded applications for this technology, such as myriad “chat” agents adept at understanding natural human language. While state of the art generative models can parse text queries from a user and provide comprehensive, accurate responses (including generating images depicting desired content), current implementations struggle with understanding all information present in images of documents, especially images of business documents. In particular, generative models fail to understand structured and semi-structured information, e.g., as indicated by graphical information such as lines, geometric relationships (e.g., indicated by tables, graphs, figures, etc.), formatting, and other contextual information that human readers easily and implicitly understand.

Abstract: Recent developments in machine learning (commonly coined “artificial intelligence” or “AI”) have vastly expanded applications for this technology, such as myriad “chat” agents adept at understanding natural human language. While state of the art generative models can parse text queries from a user and provide comprehensive, accurate responses (including generating images depicting desired content), current implementations struggle with understanding all information present in images of documents, especially images of business documents. In particular, generative models fail to understand structured and semi-structured information, e.g., as indicated by graphical information such as lines, geometric relationships (e.g., indicated by tables, graphs, figures, etc.), formatting, and other contextual information that human readers easily and implicitly understand.

Abstract: According to one embodiment, a method for detecting, extracting information from, and classifying tables within an original image includes: pre-processing the original image to generate processed image data; detecting one or more tables within the processed image data; extracting the one or more tables from the processed image data; and classifying either: the one or more extracted tables; portions of the one or more extracted tables; or a combination thereof. Additional techniques for pre-processing image data to facilitate detection, extraction of information from, and classification of tables (or portions thereof) are also featured. Corresponding systems and computer program products are included in the scope of the invention. The inventive concepts are also applicable to tabular data arrangements that may not fit a strict definition of a “table.

Abstract: According to one embodiment, a computer-implemented method for classifying one or more tables and/or one or more tabular data arrangements depicted in image data includes: training a machine learning model, using a training dataset representing a plurality of different tables and/or tabular data arrangements, based at least in part on a plurality of recognized textual elements within the training dataset; and outputting a trained classification model based on the training, wherein the trained classification model is configured to classify one or more tables and/or one or more tabular data arrangements represented within a test dataset according to: one or more table classifications; one or more tabular data arrangement classifications; and/or one or more column classifications; and classifying the one or more tables and/or the one or more tabular data arrangements represented within the test dataset using the trained classification model.

Abstract: According to one embodiment, a computer-implemented method for detecting and classifying columns of tables and/or tabular data arrangements within image data includes: detecting one or more tables and/or one or more tabular data arrangements within the image data; extracting the one or more tables and/or the one or more tabular data arrangements from the processed image data; and classifying either: a plurality of columns of the one or more extracted tables; a plurality of columns of the one or more extracted tabular data arrangements; or both the columns of the one or more extracted tables and the columns of the one or more extracted tabular data arrangements. Corresponding systems and computer program products are also disclosed.

Abstract: According to one embodiment, a computer-implemented method for classifying one or more tables and/or one or more tabular data arrangements depicted in image data includes: training a machine learning model, using a training dataset representing a plurality of different tables and/or tabular data arrangements, based at least in part on a plurality of recognized textual elements within the training dataset; and outputting a trained classification model based on the training, wherein the trained classification model is configured to classify one or more tables and/or one or more tabular data arrangements represented within a test dataset according to: one or more table classifications; one or more tabular data arrangement classifications; and/or one or more column classifications; and classifying the one or more tables and/or the one or more tabular data arrangements represented within the test dataset using the trained classification model.

Abstract: According to one embodiment, a method for detecting, extracting information from, and classifying tables within an original image includes: pre-processing the original image to generate processed image data; detecting one or more tables within the processed image data; extracting the one or more tables from the processed image data; and classifying either: the one or more extracted tables; portions of the one or more extracted tables; or a combination thereof. Additional techniques for pre-processing image data to facilitate detection, extraction of information from, and classification of tables (or portions thereof) are also featured. Corresponding systems and computer program products are included in the scope of the invention. The inventive concepts are also applicable to tabular data arrangements that may not fit a strict definition of a “table.