Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving an image frame representing a scene and extracting a first image patch of the image frame. The first image patch includes a first plurality of pixels representing a signboard including information in the scene. The operations further include determining, based on the first image patch, each symbol of a plurality of symbols included in the information and an order in which the plurality of symbols are arranged on the signboard. Other aspects and features are also claimed and described.

Abstract: Various embodiments include methods for generating image datasets for training an artificial intelligence machine learning (AI/ML) optical character recognition (OCR) model. Image processing may be performed on a plurality of roadway images to identify street signs within the images and generate a dataset of sign images categorized into sign variants of the same shape, color, pictogram, and characters. An OCR model may process sign images to obtain OCR results for images of each sign variant. An aggregation process may be performed on the OCR results for all sign images within each sign variant to identify a ground truth OCR result for each sign variant. The ground truth OCR result may be used to automatically label all sign images of each sign variant to produce an OCR model training dataset. The produced training dataset may then be used to retrain the initial AI/ML OCR model and/or train other AI/ML OCR models.

Abstract: Various embodiments include methods for generating image datasets for training an artificial intelligence machine learning (AI/ML) optical character recognition (OCR) model. Image processing may be performed on a plurality of roadway images to identify street signs within the images and generate a dataset of sign images categorized into sign variants of the same shape, color, pictogram, and characters. An OCR model may process sign images to obtain OCR results for images of each sign variant. An aggregation process may be performed on the OCR results for all sign images within each sign variant to identify a ground truth OCR result for each sign variant. The ground truth OCR result may be used to automatically label all sign images of each sign variant to produce an OCR model training dataset. The produced training dataset may then be used to retrain the initial AI/ML OCR model and/or train other AI/ML OCR models.