Xerox Corporation

Conduent Business Services, LLC

Claude Fillion

Aaron Michael Burry

Peter Paul

Vishal Monga

Vladimir Kozitsky

Zhigang Fan

Michael Robert Furst

Naveen G Sharma

Tracy Eugene Thieret

Robert J St Jacques, Jr

Mary Catherine McCorkindale

Michael P Kehoe

Arturo M Lorenzo

Michael F Cavanaugh

Weixia Huang

Ramesh Nagarajan

Lalit Keshav Mestha

Raja Bala

Marc Dennis Daniels

John C Austin

Ronald M Rockwell

Christopher Jon Regruit

Loranzo Whitfield

Stuart Alan Schweid

Howard A Mizes

Orhan Bulan

R Enrique Viturro

Brian Robert Conrow

Tonya L Love

William Joseph Hannaway

Douglas Raymond Taylor

A method, system, and computer-usable tangible storage device for robustly cropping and accurately recognizing license plates to account for noise sources and interfering artifacts are disclosed. License plate images and sub-images can be tightly cropped utilizing an image-based classifier and gradient-based cropping. An image-based classifier can identify the location of valid characters within the image. Because of a number of noise sources, such as, for example, residual plate rotation and shear in the characters within the image, the image-based classifier performs a 'rough' identification of the image boundaries. An additional processing step utilizing gradient-based cropping is performed to fine-tune the license plate image boundaries. Gradient-based cropping eliminates unwanted border artifacts that could substantially impact the segmentation and license plate character recognition results.

Robust cropping of license plate images

Methods and systems for bootstrapping an OCR engine for license plate recognition. One or more OCR engines can be trained utilizing purely synthetically generated characters. A subset of classifiers, which require augmentation with real examples, along how many real examples are required for each, can be identified. The OCR engine can then be deployed to the field with constraints on automation based on this analysis to operate in a 'bootstrapping' period wherein some characters are automatically recognized while others are sent for human review. The previously determined number of real examples required for augmenting the subset of classifiers can be collected. Each subset of identified classifiers can then be retrained as the number of real examples required becomes available.

Method and system for bootstrapping an OCR engine for license plate recognition

Methods and systems for character segmentation in an automatic license plate recognition application. One or more images of a license plate are acquired. Then, a pixel-level importance may be calculated with respect to the image(s) of the license plate based on information within the image, such as gradient information and raw grayscale information. A seam selection can be then applied with respect to the pixel-level importance map and the image(s) by enforcing constraints based on known characteristics of license plates in order to provide for character segmentation with respect to the image(s) of the license plate.

Methods and systems for character segmentation in automated license plate recognition applications

Methods, systems and processor-readable media for adaptive character segmentation in an automatic license plate recognition application. A region of interest can be identified in an image of a license plate acquired via an automatic license plate recognition engine. Characters in the image with respect to the region of interest can be segmented using a histogram projection associated with particular segmentation threshold parameters. The characters in the image can be iteratively validated if a minimum number of valid characters is determined based on the histogram projection and the particular segmentation threshold parameters to produce character images sufficient to identify the license plate.

Adaptive character segmentation method and system for automated license plate recognition

A method and system for achieving accurate segmentation of characters with respect to a license plate image within a tight bounding box image. A vehicle image can be captured by an image capturing unit and processed utilizing an ALPR unit. A vertical projection histogram can be calculated to produce an initial character boundary (cuts) and local statistical information can be employed to split a large cut and insert a missing character. The cut can be classified as a valid and/or a suspect character and the suspect character can be analyzed. The suspect character can be normalized and passed to an OCR module for decoding and generating a confidence quote with every conclusion. The non-character images can be rejected at the OCR level by enforcing a confidence threshold. An adjoining suspect narrow character can be combined and the OCR confidence of the combined character can be assessed.

Robust character segmentation for license plate images

What is disclosed is a novel system and method for determining printer component velocity variations by analyzing multiple page test patterns. A test pattern, such as ladder chart targets, is produced that extends across multiple pages. Corresponding page sync signals are recorded and used to maintain phase coherence when analyzing scanned images associated with the multiple pages. An algorithm determines the ladder rung positions and the average photoreceptor velocity between each ladder rung on each scanned image for each page. Interpolation is used for proper phase alignment of the velocity data that spans multiple pages. The long assembly of phase coherent velocity data is then analyzed in one embodiment to determine its frequency content and to estimate the photoreceptor motion quality error sources. Based upon these estimated error sources, a trouble condition or pending maintenance problem with the printer is able to be indentified.

Photoreceptor motion quality estimation using multiple sampling intervals

A method for determining a confidence level to be used in identifying a vehicle. The method includes receiving a vehicle image, extracting a license plate image from the at least one vehicle image, determining a license plate number and associated confidence level based upon the license plate image, and comparing the associated confidence level against a confidence threshold. If the associated confidence level is below the confidence threshold, the method further includes extracting auxiliary data from the at least one vehicle image, corresponding the extracted auxiliary data and a set of stored auxiliary data, and updating the associated confidence level to produce an updated confidence level based upon the correspondence of the extracted auxiliary data and the set of stored auxiliary data.

Method for automatic license plate recognition using adaptive feature set

What is disclosed is a resizing method that utilizes segmentation information to classify objects found within a document and then selects the most appropriate resizing technique for each identified object. The present method employs readily available document parsers to reliably extract objects. e.g. text, background, images, graphics, etc., which compose the document. Information obtained from a document parser is utilized to identify the document components for classification. The extracted objects are then classified according to their object type. Each of classified objects are then resized using a resizing technique having been pre-selected for the object type based on their respective abilities to resize certain types of document content over other resizing techniques. The present method advantageously extends smart or content-based scaling and is especially useful for N-up or variable-information printing. The present method finds its intended uses in enhancing N-up and handout options currently provided in a variety of print-drivers.

Object based adaptive document resizing

Streak compensation in a digital printer is provided utilizing a spatially varying Printer Model and Run Time updates to generate Spatially Varying Tone Reproduction Curves (STRCs) which are used as actuators to compensate for streaks during run time. A full width array sensor is used to measure streak profiles and the STRCs are used as actuators to compensate for streaks. Streak profile measurements taken at a limited number of area coverage levels combined with a Printer Streaks Basis Function Model are used to estimate and project the streak behavior at all area coverage levels and at all inboard-to-outboard spatial locations. The projection is then used in a pixel-wise error feedback control scheme to drive each profile to a desired shape, thereby compensating for streaks.

Streak compensation using model based projections for run time updates

A system resizes content within a document that includes a document segmenter that receives a document that contains content. The document segmenter analyzes the content within the document and segments the content into a plurality of object types. An object priority applicator determines a class value associated with each object type. A location scaler identifies a datum point for each object type within the document, wherein each datum point maintains a relative location to one another regardless of document resizing. An object sizing component resizes each object based at least in part upon the class value.

Systems and methods to resize document content

A system and method for resizing a digitally represented color image are presented. A color image with pixels defined by luminance and at least one chrominance value is received. For each pixel of the color image, a luminance spatial variation and respective chrominance spatial variations in the respective neighborhood of the each pixel are computed. The luminance spatial variation and the respective chrominance spatial variations are combined to produce a respective importance value for each pixel. Selected pixels are identified based upon their respective importance values and are removed by seam carving of the color image. The seam carving identifies seams of pixels based upon the respective importance values of pixels within the seams of pixels to create a resized color image. The resized color image is produced to an image output device.

Smart image resizing with color-based entropy and gradient operators

What is disclosed is a system and method for performing a background deletion that exploits both local and global context to remove background and other white space between objects with the aim of retaining structural relationships between objects in the document. A document image is received and seams are carved through the image. Seams composed of uniform background pixels are identified. Adjacent seams containing background pixels are collected into groups of seams. The background seam groups are classified according to their widths. A target number of seams to be removed for each background seam group is then determined based on the classification. Seam groups which are wider will have at least the same or a greater target number of seams to be deleted therefrom than will seam groups of narrower widths. The document image is then resized by deleting seams from the seam groups based on the assigned target number.

Resizing a digital document image via background content removal

What is disclosed is a method for iterative seam selection in an image resizing system utilizing a seam carving technique. In one embodiment, an importance map is generated for a received source image. Seams are carved through the image from one edge to an opposite edge. An energy is computed for each seam based on pixel importance values. A distance is computed from each seam to a previously selected seam. A weighting for each seam is computed using a defined weighting function and the calculated seam distances. The weighting is applied to the energy of each seam produce a revised energy for each seam. A seam is selected based on the produced revised energy. The image is resized at a location of the selected seam. The process repeats until the image has been resized to a desired target output dimension. In such a manner, unnatural image resizing results are avoided.

Iterative selection of pixel paths for content aware image resizing

A class of add-on system components providing functionality of devices, management of self-help, customization and enhancement of services for devices, and remote monitoring at significantly reduced cost. It reduces the cost of UI and greatly enhances ease-of-use by realizing that the networked system can be headless and use a browser-based UI for its platform. It embeds a Device Model Agent (DMA) and connects to the Device-Centric Services platform to offer services. Additionally, it offers customized diagnostic access for customer representatives to enable self-help service actions to be performed, unifies and manages multiple access needs to several disparate data access mechanisms, and uses largely COTS PC hardware rather than customized PWBs.

Growing community of inventors

Rochester, NY, United States of America

Claude Fillion