License plate recognition (“LPR”) systems may encounter degradation in quality and obstructions that negatively impact performance of the LPR systems. A LPR system is configured to apply image processing algorithms to output information describing performance of the system and to monitor the performance of the system over time. Based on the performance of the system over time, the LPR system determines when one or more entities of the system require action to maintain or improve performance and transmits information describing the required action.

The present disclosure relates to a system and computer-implemented method for character recognition in a payment card. The method includes receiving an image of a payment card and one or more details associated with the payment card. Further, a derivative of the image is determined based on the one or more details and a horizontal sum of pixel values is determined for a plurality of rows in the image. Furthermore, one or more Regions of Interest (ROIs) are identified in the image by comparing the horizontal sum of pixel values with a predefined first threshold. Subsequently, one or more characters in the one or more ROIs are extracted using one or more peak values in a histogram of the one or more ROIs. Finally, each of the one or more characters extracted from the one or more ROIs is recognized using a trained Artificial Intelligence technique.

An image processing method for identifying text on production line components obtains an image to be recognized and a standard image for reference and extracts a first text area of the image to be recognized. A second text area of the standard image is obtained, and a text window is extracted based on the second text area. The method further obtains a target text area of the image to be recognized based on the first text area and the text window, and obtains a first set of first text sub-areas, and obtains a second set of second text sub-areas, by dividing the second text area into sub-windows of the text window. The method further marks the image to be recognized as a qualifying image when each first text sub-area of the first set is the same as a corresponding second text sub-area of the second set.

An image processing method for identifying text on production line components obtains an image to be recognized and a standard image for reference and extracts a first text area of the image to be recognized. A second text area of the standard image is obtained, and a text window is extracted based on the second text area. The method further obtains a target text area of the image to be recognized based on the first text area and the text window, and obtains a first set of first text sub-areas, and obtains a second set of second text sub-areas, by dividing the second text area into sub-windows of the text window. The method further marks the image to be recognized as a qualifying image when each first text sub-area of the first set is the same as a corresponding second text sub-area of the second set.

The present disclosure relates to an apparatus and method for controlling an autonomous vehicle to allow an autonomous vehicle to safely pass through a road according to a driver's choice when the width of the road is narrow. The apparatus includes a sensor for acquiring information data of obstacles and vehicles in front of and on a side of a host vehicle, a signal processor for outputting data with respect to positions and media of obstacles and a determination signal representing presence or absence of a vehicle on a driving path, a controller for determining whether driving is possible by analyzing information acquired by the sensor and outputting a control signal corresponding to a selection signal of the driver, an interface for displaying an image processed by the signal processor, and an autonomous driving function unit for performing autonomous driving according to the control signal.

In some implementations, a transaction card security system may detect a transaction card in an image, and may perform image processing to identify one or more character sequences or one or more designs that appear on the transaction card. The one or more character sequences or the one or more designs may identify an institution associated with the transaction card and/or a cardholder associated with the transaction card. The transaction card security system may identify the institution or the cardholder associated with the transaction card based on the one or more character sequences or the one or more designs, and may perform one or more actions relating to security of the transaction card based on detecting the transaction card in the image and identifying the institution or the cardholder associated with the transaction card.

In some implementations, a transaction card security system may detect a transaction card in an image, and may perform image processing to identify one or more character sequences or one or more designs that appear on the transaction card. The one or more character sequences or the one or more designs may identify an institution associated with the transaction card and/or a cardholder associated with the transaction card. The transaction card security system may identify the institution or the cardholder associated with the transaction card based on the one or more character sequences or the one or more designs, and may perform one or more actions relating to security of the transaction card based on detecting the transaction card in the image and identifying the institution or the cardholder associated with the transaction card.

A system for digitizing gauges, lights and other human-readable machine gauges and functions and status without interfering with the operation of the machine or requiring re-working or interfering with the existing machine wiring, signaling, electrical or mechanical elements or operating modes, or adding new digitizing equipment to the machine.

A computer implemented method of performing single pass optical character recognition (OCR) including at least one fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The steps include preprocessing an input image, extracting image features from the input image, determining at least one optical character recognition feature, building word boxes using the at least one optical character recognition feature, determining each character within each word box based on character predictions and transmitting for display each word box including its predicted corresponding characters.

A computer implemented method of performing single pass optical character recognition (OCR) including at least one fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The steps include preprocessing an input image, extracting image features from the input image, determining at least one optical character recognition feature, building word boxes using the at least one optical character recognition feature, determining each character within each word box based on character predictions and transmitting for display each word box including its predicted corresponding characters.