Abstract: The technologies regarding dynamic image-based parking systems and methods of operation thereof. An example method for operating a camera-based parking management system includes: activating an AI-enabled camera covering a parking area responsive to detecting a parking object; capturing a snapshot of the parking area using the AI-enabled camera; transmitting the snapshot to an edge processor to identify parking object(s) shown in the snapshot; determining that the parking object shown in the snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model; responsive to the determining: identifying first one or more characteristics about the parking object that potentially reduce identification accuracy; and identifying second one or more characteristics about the snapshot (e.g.

Abstract: Methods and systems for manual-based image review can involve associating an image with a group of keyword utterances, the image displayable in a display screen of a computing device, the group of keyword utterances including different keyword utterances. A prompt for the user to utter a keyword utterance can be displayed in a first area of the image in the display screen and another prompt for the user to utter another keyword utterance can be displayed in another area of the image in the display screen. Audio of the keyword utterances displayed in the display screen can be captured and processed by natural language processing (NLP), when uttered by the user. The utterances can be displayed respectively as text in the first area and the other area of the image in response to processing by NLP of the audio. Thus, instead of users typing in the results and changing their focus between screen and keyboard, for example, the user can speak to the results, which increases the throughput of the results.

Abstract: Methods and systems for automatic license plate recognition (ALPR) can involve obtaining results from a manual image review of an image captured by one or more ALPR cameras and processed by an ALPR engine and feeding the results from the manual image review back to the ALPR engine to train the ALPR engine where to search in the image for a license plate. Training of the ALPR engine increase the attach rate of an optical character recognition (OCR) engine with respect to images captured the ALPR camera(s) and processed by the ALPR engine.

Abstract: A system and method for providing consolidated events in a traffic management control centre may include: obtaining a plurality of raw events, each from an event source and each including at least one of a geographical location, timestamp and event type; validating each of the raw events against a set of rules; terminating the raw events for which validation is not successful and leaving validated events; obtaining context data from a plurality of georeferenced dynamic map layers; matching each of the validated events with the georeferenced dynamic map layers using the at least one of geographical location, timestamp and event type, and enriching each of the validated events with the associated context data to generate enriched events; matching each of the enriched events against a plurality of stored events to generate consolidated events; and providing at least one of the consolidated events to a user.

Abstract: The disclosed invention includes systems and methods for training and employing equivariant models for generating representations (e.g., vector representations) of temporally-varying content, such as but not limited to video content. The trained models are equivariant to temporal transformations applied to the input content (e.g., video content). The trained models are additionally invariant to non-temporal transformations (e.g., spatial and/or color-space transformations) applied to the input content. Such representations are employed in various machine learning tasks, such as but not limited to video retrieval (e.g., video search engine applications), identification of actions depicted in video, and temporally ordering clips of the video.

Abstract: Systems can be configured for detecting license plates and recognizing characters in license plates. In an example, a system can receive an image and identify one or more regions in the image that include a license plate. Character recognition can be performed in the one or more regions to determine contents of a candidate license plate. Location-specific information about a license plate format can be used together with the determined contents of the candidate license plate to determine if the recognized characters are valid.

Abstract: Systems and methods for cloud-based device management. In one implementation, a processing device receives one or more images captured in relation to a device, processes the one or more images to determine a presence of one or more identifiers within the one or more images, based on a determination that an identifier is present within the one or more images, processes the identifier in relation to an information repository to determine a permission status with respect to the identifier; identifies an instruction protocol associated with the device, generates an instruction corresponding to the permission status and the instruction protocol, and provides the instruction to the device.

Abstract: A method includes receiving from a first detection system, an image of a license plate, applying an optical recognition function to characters of the license plate, and assigning a confidence value to each of the characters. The method further includes, in response to determining that the confidence value for a particular character is below a threshold, sending an image of the character to a gamified human reviewer application, receiving from the gamified human reviewer application, a human response indicative of a characteristic of the character, and updating the optical recognition function based on the characteristic of the character.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving multiple images from a camera, each image of the multiple images representative of a detection of an object within the image. For each image of the multiple images the methods include: determining a set of detected objects within the image, each object defined by a respective bounding box, and determining, from the set of detected objects within the image and ground truth labels, a false detection of a first object. The methods further include determining that a target object threshold is met based on a number of false detections of the first object in the multiple images, generating, based on the number of false detections for the first object meeting the target object threshold, an adversarial mask for the first object, and providing, to the camera, the adversarial mask.

Abstract: System and methods are disclosed for rapid license plate reading. A first image having a first resolution may be generated. A location of a license plate in the first image may be detected. The license plate may be read from a second image in accordance with the location of the license plate. The second image may have a second resolution greater than the first resolution. In embodiments, reading the license plate may comprise tracking the license plate across a plurality of license plate images.

Abstract: A process for validating a public safety agency command issued to a vehicle. In operation, a public safety agency controller receives a public safety agency command validation inquiry from a vehicle communication device. The inquiry indicates that the vehicle communication device has detected a public safety agency command issued to the vehicle and includes a first vehicle identifier and a first vehicle location. The controller receives a public safety agency command information including a second vehicle identifier and a second vehicle location from a public safety agency communication device. The validity of the public safety agency command is determined based on comparing the first vehicle identifier with the second vehicle identifier and comparing the first vehicle location with the second vehicle location. The controller then transmits a notification to the vehicle communication device identifying the validity of the public safety agency command.

Abstract: A blockchain-based platform system for interworking with one machine-to-machine (oneM2M) and lightweight machine-to-machine (LWM2M) and a method of implementing a blockchain-based platform are provided. The blockchain-based platform system for interworking with oneM2M and LWM2M includes a hyperledger fabric network configured to validate chaincode called in association with a first client application and generate an event in association with the validated chaincode, and a fabric bridge gateway configured to perform interworking with an LWM2M protocol and a oneM2M platform by transmitting a request or a response corresponding to the event to a second client application.

Abstract: A method for intelligently measuring vehicle speed based on a binocular stereo vision system includes: training a Single Shot Multibox Detector neural network to obtain a license plate recognition model; calibrating the binocular stereo vision system to acquire parameters of two cameras; detecting the license plates in the captured video frames with the license plate recognition model, locating the license plate position; performing feature point extraction and stereo matching by a feature-based matching algorithm; screening and eliminating the matching point pairs, and reserving the coordinates of the matching point pair closest to the license plate center; performing stereo measurement on the screened matching point pair to get the spatial coordinates of the position; calculating and obtaining the speed of the target vehicle.

Abstract: System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.

Abstract: A vehicle number identification device includes a registration-number-information acquisition part configured to acquire registration number information associated with an identification media installed in a vehicle; a number-plate-image acquisition part configured to acquire a number-plate image of the vehicle; an OCR processing part configured to acquire OCR resultant information representing a result of an optical character recognition with respect to the number-plate image; an estimation processing part configured to input the number-plate image into a machine-learning model and to acquire estimation result information output from the machine-learning model; and a matching determination part configured to determine whether at least two types of information out of the registration number information, the OCR resultant information, and the estimation result information indicate a same vehicle number and to identify the same vehicle number as a vehicle number of the vehicle.

Abstract: A system and method for license plate recognition is provided in the present disclosure. The method may include obtaining an image including a license plate mounted on a vehicle, and identifying at least one feature point associated with the vehicle. The method may also include determining an approximate angle range within which a tilt angle of the license plate is located based on the at least one feature point, and determining the tilt angle of the license plate within the approximate angle range. The method may further include performing a tilt correction on the license plate based on the tilt angle of the license plate.

Abstract: A method for masking objects contained in an image is provided, wherein pixel values are determined for pixels of an image. An image mask is generated, which comprises at least one first region in which pixels of a predefined object class are arranged and at least one second region in which pixels of said predefined object class are not arranged. The distance to the nearest pixel in the second region is determined for pixels in the at least one first region. Pixels in the at least one first region are assigned a window that has a window size that depends on the determined distance. At least one new pixel value is determined for a pixel from the pixel values of the pixels within the window assigned to the pixel. The original pixel values of pixels in the one first region are replaced with the respective new pixel values.

Abstract: An image processing method, for an identity document that comprises a data page, comprises acquiring a digital image of the page of data of the identity document. The method further comprises assigning a class or a super-class to the candidate identity document via automatic classification of the digital image by a machine-learning algorithm trained beforehand on a set of reference images in a training phase; processing the digital image to obtain a set of at least one intermediate image the weight of which is lower than or equal to the weight of the digital image; applying discrimination to the intermediate image using a discriminator neural network; and generating an output signal as output from the discriminator neural network, the value of which is representative of the probability that the candidate identity document is an authentic document or a fake.

Abstract: An image processing apparatus obtains first image data as correct image data, and second image data generated by deteriorating image quality of the first image data as input image data, and generates learning data that links the correct image data with the input image data, and learns a neural network using the generated learning data, so that the image processing apparatus generates a learning model used to convert input image data.

Abstract: A data acquisition system of a vehicle includes an image capture device, a communication interface, and a controller communicatively coupled to the image capture device and communicatively coupled to the communication interface. Processors of the controller are configured to calibrate an image-distance relationship value of an identified component of a first image captured by the image capture device corresponding to a known feature based on established metrics of the known feature. The processors are also configured to provide control of the vehicle or activation of an alert system of the vehicle via the communication interface based on the image-distance relationship value.

Abstract: A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.

Abstract: Disclosed is a method for training a neural network, for the recognition of a sequence of characters in an image and without a predefined format, including: a step of creating an artificial database of a plurality of sequences of characters in images, some sequences being generated randomly, other sequences being derived from transformations of sequences generated randomly; a learning step that teaches the neural network to recognize a sequence of characters in an image, directly on at least some of the sequences of the artificial database, without a preliminary subdividing of these sequences into their component characters.

Abstract: A number plate information specifying device includes an image acquisition unit that acquires a number plate image, a feature point extraction unit that extracts a feature point from the number plate image, a degree of similarity calculation unit that references a learning data set in which a plurality of feature points are recorded in association with a plurality of pieces of number plate information and calculates degrees of similarity for the feature points recorded in the learning data set that correspond to the feature point extracted from the number plate image, a vote value calculation unit that, on the basis of the degrees of similarity, calculates vote values for the pieces of number plate information recorded in the learning data set, and a specifying unit that specifies the piece of number plate information that has the highest vote value as the number plate information displayed in the number plate image.

Abstract: The present application is directed to a system and method for law enforcement incident reporting. More particularly, a mobile application is provided with multiple modules related to the law enforcement incident reporting procedures for vehicle-vehicle, vehicle-pedestrian, and vehicle-property collisions. The system and method of the present application can be used by law enforcement (LE) patrol officers to accurately and quickly document LE incidents and traffic collision reports in a fraction of time compared to the traditional reporting styles of using pen and paper. The system and method of the present application is primarily used as a mobile application and is compatible with both Android and iOS devices including smartphones, tablets, desktops, laptops, hand-held computers, etc. Once incidents/collisions are documented, LE officers can edit, store and send their respective reports.

Abstract: Systems and methods are disclosed and an example system includes a digital image receiver for receiving a digital image, and an automatic obscuration processor coupled to the image receiver and configured to determine whether the digital image includes a region that classifies as an image of a category of object and, upon a positive determination, to obscure the region and output a corresponding obscured-region digital image.

Abstract: Systems and methods for monitoring event through the use of occurrence records in order to take one or more actions are described herein. An occurrence record is received and processed. The occurrence record includes one or more parameters and are indicative of an event of a given type. The received occurrence record is compared to one or more trigger-based rules. When a trigger condition of a trigger-based rule is met, the trigger-based rule is executed. When one or more other conditions of the executed trigger-based rule is met, a command for an action is generated.

Abstract: Systems can be configured for detecting license plates and recognizing characters in license plates. In an example, a system can receive an image and identify one or more regions in the image that include a license plate. Character recognition can be performed in the one or more regions to determine contents of a candidate license plate. Location-specific information about a license plate format can be used together with the determined contents of the candidate license plate to determine if the recognized characters are valid.

Abstract: Implementations generally relate to estimating grouped observations. In some implementations, a method includes detecting a license plate of a vehicle. The method further includes capturing a plurality of images of the license plate. The method further includes computing candidate regions associated with the license plate based on the images of the license plate. The method further includes computing candidate characters of the license plate based on the candidate regions and the images of the license plate. The method further includes selecting target characters from the candidate characters based on one or more predetermined selection criteria.

Abstract: A system for image classification is disclosed that includes a central system configured to provide high reliability image data processing and recognition and a plurality of endpoint systems, each configured to provide image data processing and recognition with a lower reliability than the central system and to generate probability data. A decision switch disposed at each of the plurality of endpoint systems is configured to receive the probability data and to determine whether to deny access, grant access or generate a referral message to the central system, wherein the referral message includes at least a set of image data generated at the endpoint system.

Abstract: A customer premises parking system including one or more cameras and wireless communications transmission capability is described. The presence of a vehicle with a license plate at an entrance or exit of a parking area is detected and one or more high resolution images are captured and time stamped. A license plate portion of the HD image is captured and stored. The license plate number and corresponding time is determined and communicated using a wide area wireless network. The HD license plate portion of the image is processed to produce a low resolution version of the license plate image which is segmented and processed into fragments which are transmitted via the wide area network using available transmission opportunities. A high resolution version of the license plate portion is stored at the premises where the parking facilitate is located and can be retrieved via a cellular wireless interface in the event of a dispute or discrepancy with regard to the detected and reported license plate number.

Abstract: System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.

Abstract: A license plate recognition system includes an image capturing module, a license plate detection module, a segment extraction module, a character classification module, and a character recognition module. The image capturing module is for capturing an image. The license plate detection module is for receiving the image and to identify a license plate in the image. The segment extraction module is for extracting a sequence of character segments on the license plate. The character classification module is for computing a probability of each possible character in each character segment. The character recognition module is for identifying permissible characters for the each character segment according to a syntax of the sequence of character segments, and to identify a character having a highest probability among the permissible characters as a selected character for the each character segment.

Abstract: An information providing system includes an onboard device and a server that transmits information to and receives information from the onboard device. The onboard device includes a condition setting unit that acquires a destination and an output mode and transmits the acquired destination and the acquired output mode to the server. The onboard device further includes an output unit that outputs parking lot recommendation information which is received from the server. The server includes a storage unit that stores parking lot information including position information of parking lots and parking fees which are detected from a captured image of the parking lots. The server further includes an information providing unit that prioritizes parking lots which are located within a predetermined range from the destination based on the output mode provides the parking lot information of the prioritized parking lots as the parking lot recommendation information to the onboard device.

Abstract: An image-processing device includes: a reliability calculation unit configured to calculate reliability of a character recognition result for a document image which is a character recognition target on the basis of a descriptive feature amount of a character string of a specific item included in the document image; and an image output unit configured to output an image of the character recognition result indicating the character string of the specific item in a display mode in accordance with the reliability.

Abstract: An analysis method implemented by a computer includes: generating a refine image by changing an incorrect inference image such that a correct label score of inference is maximized, the incorrect inference image being an input image when an incorrect label is inferred in an image recognition process; and narrowing, based on a score of a label, a predetermined region to specify an image section that causes incorrect inference, the score of the label being inferred by inputting to an inferring process an image obtained by replacing the predetermined region in the incorrect inference image with the refine image.

Abstract: A system includes a camera defining an optical axis and a field of view defined about the optical axis. An illuminator is mounted offset from the optical axis and directed to illuminate at least a portion of the field of view, wherein the illuminator is operatively connected to the camera to provide illumination during an image capturing exposure of the camera. An image processer is operatively connected to the camera and includes machine readable instructions configured to receive image data representative of an image captured with the camera, perform facial detection to detect at least one face in the image, perform license plate detection to detect at least one license plate in the image, and to provide a vehicle overview image.

Abstract: Detecting pedestrians with objects is described (pushing stroller, carts, opening doors, carrying umbrellas, etc.). In an example, a perception component of a vehicle can receive sensor data from sensor(s) associated with the vehicle. The perception component can determine, by a model, observation(s) associated with the sensor data, wherein an observation comprises a first object (e.g., a pedestrian). The perception component can determine whether the first object is associated with a second object (e.g., a pedestrian object), wherein the first object and the second object are associated with a compound object (e.g., a pedestrian/pedestrian object system). The perception component can provide the indication of the first object or an indication of the compound object to at least one of a prediction component or a planning component of the vehicle for controlling the vehicle.

Abstract: A system and method for recognition of license plate numbers from Bayer-domain image data is provided. The system acquires Bayer-domain image data of a scene comprising a vehicle and inputs such image data to a Deep Neural Network (DNN) model trained for license plate detection (LPD) and license plate recognition (LPR). The system extracts an LPD result as an output of the DNN model. Based on the LPD result, the system extracts, from the input Bayer-domain image data, a region-of-interest (RoI) comprising a license plate image associated with the vehicle. The system inputs the extracted RoI to the DNN model and extracts, as an output of the DNN model, an LPR result which indicates a confidence of the DNN model in recognition of a license plate number of the vehicle from the input RoI. The system determines the license plate number based on the extracted LPR result.

Abstract: An image processing device includes: a detection unit which detects a target object based on edge information acquired from image data; and a judging unit which judges whether a certain region in the image data is an improper region that is not suitable for detection of the target object based on an edge pattern of edge information in the certain region and a feature pattern indicating a feature of the target object, and the detection unit detects the target object excluding a region that has been judged to be the improper region by the judging unit.

Abstract: The invention discloses a method for glass detection in a real scene, which belongs to the field of object detection. The present invention designs a combination method based on LCFI blocks to effectively integrate context features of different scales. Finally, multiple LCFI combination blocks are embedded into the glass detection network GDNet to obtain large-scale context features of different levels, thereby realize reliable and accurate glass detection in various scenarios. The glass detection network GDNet in the present invention can effectively predict the true area of glass in different scenes through this method of fusing context features of different scales, successfully detect glass with different sizes, and effectively handle with glass in different scenes. GDNet has strong adaptability to the various glass area sizes of the images in the glass detection dataset, and has the highest accuracy in the field of the same type of object detection.

Abstract: An identification and exclusion system and method for restricting access to recreation venues by a person who caused an incident at a recreation venue (22). The system includes a mobile communications device (12) having a camera and a GPS chip, and a computer processor (14) configured to receive an incident record (24) from the mobile communications device and broadcast over a communications network (16) the incident record to a plurality of recreation venues within a predetermined area. The incident record includes an image of the person and a record of the incident type.

Abstract: A battery powered artificial intelligence autonomous patrol vehicle. The vehicle is battery powered and is operated autonomously using artificial intelligence. The autonomous patrol vehicle includes a surveillance or detection system on or within an emergency light bar of the autonomous patrol vehicle. The autonomous patrol vehicle autonomously performs patrol surveillance or detection functions such as speed; red-light; and traffic enforcement; traffic enforcement with Automatic License Plate Recognition (ALPR), face recognition, animal recognition, object recognition, video surveillance and online continuous analysis and any equivalent surveillance or detection functions. The autonomous patrol vehicle stores and sends live and/or stored data to a command control center while moving or parked. The surveillance or detection system navigates with the use of any one of or a combination of a camera, radar and Light Detection and Ranging (LIDAR) sensors and can operate by viewing 360 degrees.

Abstract: An in-vehicle device comprises an image recognizer configured to acquire an image using a camera for photographing the exterior of a vehicle and execute processing for recognizing license plate information included in the image at intervals of a predetermined period; and a period determiner configured to determine the period to perform the recognition processing on the basis of a length of time in which a license plate of another vehicle stays within a field of view of the camera.

Abstract: Various implementations disclosed herein include devices, systems, and methods that determines changes in depth category of pixels from one image to the next. For example, changes in depth category may include determining that a given pixel has changed from foreground to background or vice versa. The technique is based on creating a histogram or other model using a mask identifying a depth category of pixels in the first image. The histogram or model is used to assess a pixel in the first frame and the second frame and identify a change in its depth category. One use of this technique is to determine a change in occlusion.

Abstract: A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.

Abstract: Provided is a smart roadside unit, including a light intensity sensor configured to generate current light intensity information; a high-bright camera assembly configured to capture a high-bright image; a low-bright camera assembly configured to capture a low-bright image; and a controller configured to turn on the high-bright camera assembly to shoot when the current light intensity is greater than a first light intensity threshold, turn on the low-bright camera assembly to shoot when the current light intensity is less than a second light intensity threshold, and extract vehicle information from the high-bright image or the low-bright image, in which the second light intensity threshold is smaller than the first light intensity threshold.

Abstract: A text recognition method, a text recognition apparatus, an electronic device, and a storage medium are provided. The method includes: extracting character prediction regions of a text line image using a constructed text recognition model, to obtain a character region set; calculating horizon-distance IOUs between the character prediction regions in the character region set according to a position of each character prediction region in the character region set; removing overlapped character prediction regions from the character region set, according to the horizon-distance IOUs between the character prediction regions in the character region set, to obtain a character position set; and sorting and outputting character categories corresponding to the character prediction regions in the character position set according to positions of the character prediction regions in the character position set.

Abstract: A method, apparatus, and computer program product are provided for determining a location of on-street parking. The method includes receiving a plurality of individual probe points along a section of a roadway. Each individual probe point is associated with a location where each probe point was captured by one of one or more vehicles on the section of the roadway. The method also includes selecting one or more of the plurality of individual probe points based on a speed of the one or more vehicles associated with each probe point at a time the probe point was captured. The method further includes comparing the location of the selected probe points with a known roadway representation. Based on the comparison, the method also includes determining one or more groupings of a plurality of potential parking spaces along the section of the roadway. A corresponding apparatus and computer program product are provided.

Abstract: An information providing system includes an onboard device and a server that transmits information to and receives information from the onboard device. The onboard device includes a condition setting unit that acquires a destination and an output mode and transmits the acquired destination and the acquired output mode to the server. The onboard device further includes an output unit that outputs parking lot recommendation information which is received from the server. The server includes a storage unit that stores parking lot information including position information of parking lots and parking fees which are detected from a captured image of the parking lots. The server further includes an information providing unit that prioritizes parking lots which are located within a predetermined range from the destination based on the output mode provides the parking lot information of the prioritized parking lots as the parking lot recommendation information to the onboard device.

Abstract: A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.