Abstract: A method, system, and computer program product for segmenting and processing documents for optical character recognition is provided. The method includes receiving a document and detecting different types of text data. The document is divided into a plurality of text regions associated with the different types of said text data. Optical noise is removed from each text region and differing optical character recognition software code is selected for application to each text region. The differing optical character recognition software code is executed with respect to each text region resulting in extractable computer readable text located within each said text region.

Abstract: A data-driven model compression technique is introduced that only targets to provide same accuracy as the original (not compressed) model in certain areas by reducing compression parameters. A compression engine relies on backpropagation to determine an extent of parameter value changes and designate certain parameters as key parameters. The model matrix is reshaped according to importance of each neuron. Only randomly generated parameter values of the reshaped parameter matrix are fine tuned to create a reliable compressed neural network model.

Abstract: A method, system, and computer program product for segmenting and processing documents for optical character recognition is provided. The method includes receiving a document and detecting different types of text data. The document is divided into a plurality of text regions associated with the different types of said text data. Optical noise is removed from each text region and differing optical character recognition software code is selected for application to each text region. The differing optical character recognition software code is executed with respect to each text region resulting in extractable computer readable text located within each said text region.

Abstract: Label-based document classification using artificial intelligence includes collecting, by one or more processors, a plurality of pre-trained classification models into a model pool and a plurality of documents into a document pool. The collected plurality of pre-trained classification models are applied in parallel to the plurality of documents in the document pool to generate a list of labels. Based on the list of labels, a final label result is generated according to which a baseline algorithm for document classification is generated by the one or more processors.