Methods and systems for pre-optimizing input data for an ocr engine or other computer-implemented analysis process

Abstract

Systems and methods for computer-implemented pre-optimization of input data before further processing thereof by a computer-implemented analyzation process, such as optical character recognition (OCR). A cooperative model is employed that combines one or more supervised-learning based inspector sub-models, and one or more filter sub-models that operating in series with the inspector sub-model(s). The inspectors first receive the input data and calculate one predicted transformation parameters then used to perform transformations on the input data. The inspector-transformed data is then passed to the filters, which derive respective convolution kernels and apply same to the inspector-transformed data before passing same to the OCR or other analyzation process. The inspectors may be pretrained with different training data. For OCR, the model is trained on minimization of normalized edit distance, the inspectors apply initial warping transformations to text images, followed by application of filtering transformations and input to the OCR engine.