Class / Patent application number | Description | Number of patent applications / Date published |
382161000 | Alphanumerics | 27 |
20090129668 | Image-recognition method and system using the same - An image-recognition method and a system using the method is disclosed, which proceeds comparison through the visual lingual characteristics according to the logic of lingual vocabulary of an image to be recognized to reduce the number of the objects to be compared, and to select at least one object. After that, similarity comparison between a graphic characteristic of the image to be recognized and at least one graphic sample corresponding to the object selected is proceeded. And then, at least one graphic sample is selected to achieve open frame image recognition. | 05-21-2009 |
20100208984 | EVALUATING RELATED PHRASES - A source keyword may be received multiple times and each time, in response, a machine-learning algorithm may be used to identify and rank respective matching-keywords that have been determined to match the source keyword. A portion or unit of content may be generated based on one of the ranked matching-keywords. The content is transmitted via a network to a client device and a user's impression of the content is recorded. The machine-learning algorithm may continue to rank matching-keywords for arbitrary source keywords while the recorded impressions and corresponding matched-keywords, respectively, are used to train the machine-learning algorithm. The training alters how the machine-learning algorithm ranks matching-keywords determined to match the source keyword. | 08-19-2010 |
20100246941 | PRECISION CONSTRAINED GAUSSIAN MODEL FOR HANDWRITING RECOGNITION - Described is a technology by which handwriting recognition is performed using a precision constrained Gaussian model (PCGM) that requires far less memory than other models such as MQDF. Offline training, such as via maximum likelihood and/or minimum classification error techniques, provides classification data. The classification data includes basis matrices that are shared by classes, along with weighting coefficients and a mean vector corresponding to each class. The base matrices and weights are obtained by expanding a precision matrix for each class. In online recognition, received handwritten input (e.g., an East Asian character) is classified into a class, based upon the per-class mean vector and weighting coefficients, and the basis matrices, by a PCGM recognizer that outputs similarity scores for candidates and a decision rule that selects the most likely class. | 09-30-2010 |
20100310159 | SEMANTIC SCENE SEGMENTATION USING RANDOM MULTINOMIAL LOGIT (RML) - A system and method are disclosed for learning a random multinomial logit (RML) classifier and applying the RML classifier for scene segmentation. The system includes an image textonization module, a feature selection module and a RML classifier. The image textonization module is configured to receive an image training set with the objects of the images being pre-labeled. The image textonization module is further configured to generate corresponding texton images from the image training set. The feature selection module is configured to randomly select one or more texture-layout features from the texton images. The RML classifier comprises multiple multinomial logistic regression models. The RML classifier is configured to learn each multinomial logistic regression model using the selected texture-layout features. The RML classifier is further configured to apply the learned regression models to an input image for scene segmentation. | 12-09-2010 |
20100329545 | METHOD AND SYSTEM FOR TRAINING CLASSIFICATION AND EXTRACTION ENGINE IN AN IMAGING SOLUTION - A method and system for automatically training a document imaging classification and extraction system that switches between a manual mode and an automatic mode based on constant monitoring. A specialized sub-system monitors and records a user interaction with the classification system during the initial manual mode and, in parallel, develops and tests a user configuration with respect to an automated processing engine. The system is capable of being shifted to the automatic mode if a desired acceptability threshold is attained and the document can then be processed automatically. Furthermore, a user can interact with the classification system if the automatic mode fails. Information concerning exception handling can be entered into a training database for continual refinement of the classification and extraction system. | 12-30-2010 |
20110096983 | DEVICES, SYSTEMS AND METHODS FOR TRANSCRIPTION SUGGESTIONS AND COMPLETIONS - Methods, devices and systems are described for transcribing text from artifacts to electronic files. A computer system is provided, wherein the computer system comprises a computer-readable storage device. An image of the artifact is received wherein text is present on the artifact. A first portion of the text is analyzed. Characters representing the first portion of the text are identified at a first confidence level equal to or greater than a threshold confidence level. The characters representing the first portion of the text are stored. A second portion of the text appearing on the artifact is analyzed. A plurality of candidates to represent the second portion of the text are identified at a second confidence level below the threshold confidence level. Finally, the plurality of candidates to a user for selection are presented. | 04-28-2011 |
20110262033 | COMPACT HANDWRITING RECOGNITION - One or more techniques and/or systems are disclosed for constructing a compact handwriting character classifier. A precision constrained Gaussian model (PCGM) based handwriting classifier is trained by estimating parameters for the PCGM under minimum classification error (MCE) criterion, such as by using a computer-based processor. The estimated parameters of the trained PCGM classifier are compressed using split vector quantization (VQ) (e.g., and in some embodiments, scalar quantization) to compact the handwriting recognizer in computer-based memory. | 10-27-2011 |
20110268351 | AFFINE DISTORTION COMPENSATION - One or more techniques and/or systems are disclosed for compensating for affine distortions in handwriting recognition. Orientation estimation is performed on a handwriting sample to generate a set of likely characters for the sample. An estimated affine transform is determined for the sample by applying hidden Markov model (HMM) based minimax testing to the sample using the set of likely characters. The estimated affine transform is applied to the sample to compensate for the affine distortions of the sample, yielding an affine distortion compensated sample. | 11-03-2011 |
20110286662 | SYSTEM FOR BUILDING A PERSONALIZED-CHARACTER DATABASE AND METHOD THEREOF - Input personal handwriting of a character stored in a system character database into an input device. Divide the personal handwriting of the character into a group of personalized roots. Store the group of personalized roots in a personalized-root database. Form a plurality of personalized characters according to a plurality of personalized roots stored in the personalized-root database. Store the plurality of personalized characters in a personalized-character database. | 11-24-2011 |
20120189194 | MITIGATING USE OF MACHINE SOLVABLE HIPS - One or more techniques and/or systems are disclosed for mitigating machine solvable human interactive proofs (HIPs). A classifier is trained over a set of one or more training HIPs that have known characteristics for OCR solvability and HIP solving pattern from actual use. A HIP classification is determined for a HIP (such as from a HIP library used by a HIP generator) using the trained classifier. If the HIP is classified by the trained classifier as a merely human solvable classification, such that it may not be solved by a machine, the HIP can be identified for use in the HIP generation system. Otherwise, the HIP can be altered to (attempt to) be merely human solvable. | 07-26-2012 |
20120237118 | IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND IMAGE PROCESSING PROGRAM - An image processing method is used to detect a letter by using a classifier generated through statistical learning of handling a sample image of a fixed size as supervised data, and includes the following steps. A conversion step acquires a converted image by geometrically converting a target image containing a letter to be detected such that the target image has a predetermined ratio defining an aspect ratio. A search step searches the converted image for one or more letter candidates each including a region of a possible letter by using the classifier. An integration step applies clustering to the letter candidates, integrating the letter candidates, and eliminates the letter candidate having low reliability A circumscribing step cuts a letter out of the letter candidate that has been integrated and has not been eliminated, and generates a rectangle circumscribing the letter. | 09-20-2012 |
20120314941 | ACCURATE TEXT CLASSIFICATION THROUGH SELECTIVE USE OF IMAGE DATA - Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers. Certain implementations are also directed to learning a number of three-way image classifiers focused only on “confusing categories” of the text signals to improve upon those specific areas where text-only classification is weakest. | 12-13-2012 |
20130044943 | Classifier Combination for Optical Character Recognition Systems - Techniques and methods are disclosed herein for combining and weighting of values from and associated with classifiers. Classifiers are used to recognize characters as part of an optical character recognition (OCR) system. Various methods of normalization facilitate combining of results of classifiers. For example, weight values may be entered into a weight table having two columns, one that includes weights from comparing patterns with images of correct characters, the other column includes weights from comparing patterns with images of incorrect characters. | 02-21-2013 |
20130114890 | SYSTEM AND METHOD FOR SEGMENTING TEXT LINES IN DOCUMENTS - Methods and systems of the present embodiment provide segmenting of connected components of markings found in document images. Segmenting includes detecting aligned text. From this detected material an aligned text mask is generated and used in processing of the images. The processing includes breaking connected components in the document images into smaller pieces or fragments by detecting and segregating the connected components and fragments thereof likely to belong to aligned text. | 05-09-2013 |
20130251249 | ROTATION-FREE RECOGNITION OF HANDWRITTEN CHARACTERS - A character recognition system receives an unknown character and recognizes the character based on a pre-trained recognition model. Prior to recognizing the character, the character recognition system may pre-process the character to rotate the character to a normalized orientation. By rotating the character to a normalized orientation in both training and recognition stages, the character recognition system releases the pre-trained recognition model from considering character prototypes in different orientations and thereby speeds up recognition of the unknown character. In one example, the character recognition system rotates the character to the normalized orientation by aligning a line between a sum of coordinates of starting points and a sum of coordinates of ending points of each stroke of the character with a normalized direction. | 09-26-2013 |
20130315480 | MATCHING TEXT TO IMAGES - Text in web pages or other text documents may be classified based on the images or other objects within the webpage. A system for identifying and classifying text related to an object may identify one or more web pages containing the image or similar images, determine topics from the text of the document, and develop a set of training phrases for a classifier. The classifier may be trained and then used to analyze the text in the documents. The training set may include both positive examples and negative examples of text taken from the set of documents. A positive example may include captions or other elements directly associated with the object, while negative examples may include text taken from the documents, but from a large distance from the object. In some cases, the system may iterate on the classification process to refine the results. | 11-28-2013 |
20140105488 | LEARNING-BASED IMAGE PAGE INDEX SELECTION - Architecture that performs image page index selection. A learning-based framework learns a statistical model based on the hyperlink (URL-uniform resource locator) previous click information obtained from the image search users. The learned model can combine the features of a newly discovered URL to predict the possibility of the newly-discovered URL being clicked in the future image search. In addition to existing web index selection features, image clicks are added as features, and the image clicks are aggregated over different URL segments, as well as the site modeling pattern trees to reduce the sparse problem of the image click information. | 04-17-2014 |
20140169665 | Automated Processing of Documents - A system and method for processing documents with automatic improvements to the processing. Documents are submitted to a processing system and data is extracted from the documents. The data may be extracted utilising OCR techniques. The data may be verified and interpreted utilising classifiers and predefined feature extraction rules which may improve their performance through an iterative learning cycle. | 06-19-2014 |
20140177951 | METHOD, APPARATUS, AND STORAGE MEDIUM HAVING COMPUTER EXECUTABLE INSTRUCTIONS FOR PROCESSING OF AN ELECTRONIC DOCUMENT - In a method for processing an electronic document, a database which is used to extract information relating to the document is adapted using the electronic document, and in which the database is adapted using at least one item of feedback from a user. Furthermore, an apparatus, a computer program product and a storage medium are accordingly specified. | 06-26-2014 |
20140270497 | ACCURATE TEXT CLASSIFICATION THROUGH SELECTIVE USE OF IMAGE DATA - Product images are used in conjunction with textual descriptions to improve classifications of product offerings. By combining cues from both text and image descriptions associated with products, implementations enhance both the precision and recall of product description classifications within the context of web-based commerce search. Several implementations are directed to improving those areas where text-only approaches are most unreliable. For example, several implementations use image signals to complement text classifiers and improve overall product classification in situations where brief textual product descriptions use vocabulary that overlaps with multiple diverse categories. Other implementations are directed to using text and images “training sets” to improve automated classifiers including text-only classifiers. Certain implementations are also directed to learning a number of three-way image classifiers focused only on “confusing categories” of the text signals to improve upon those specific areas where text-only classification is weakest. | 09-18-2014 |
20150139539 | APPARATUS AND METHOD FOR DETECTING FORGERY/FALSIFICATION OF HOMEPAGE - An apparatus and method for detecting forgery/falsification of a homepage. The apparatus includes a homepage image shot generation module for generating homepage image shots of an entire screen of an accessed homepage. A character string extraction module extracts character strings from each homepage image shot using an OCR technique. A character string comparison module compares each of the extracted character strings with character strings required for determination of homepage forgery/falsification, thus determining whether the extracted character string is a normal character string or a falsified character string. A homepage falsification determination module determines whether the corresponding homepage has been forged/falsified, based on results of the comparison. A character string learning module learns the character string extracted from the homepage image shot, based on results of the determination, and classifies the character string as the normal character string or the falsified character string. | 05-21-2015 |
20150339525 | PROCESS OF HANDWRITING RECOGNITION AND RELATED APPARATUS - Process, and related apparatus, that exploits psycho-physiological aspects involved in generation and perception of handwriting for directly inferring from the trace on the paper (or any other means on which the author writes by hand) the interpretation of writing, i.e. the sequence of characters that the trace is intended to represent. | 11-26-2015 |
20160012315 | CONTEXT-AWARE HANDWRITING RECOGNITION FOR APPLICATION INPUT FIELDS | 01-14-2016 |
20160063325 | HIERARCHICAL CLASSIFICATION IN CREDIT CARD DATA EXTRACTION - Embodiments herein provide computer-implemented techniques for allowing a user computing device to extract financial card information using optical character recognition (“OCR”). Extracting financial card information may be improved by applying various classifiers and other transformations to the image data. For example, applying a linear classifier to the image to determine digit locations before applying the OCR algorithm allows the user computing device to use less processing capacity to extract accurate card data. The OCR application may train a classifier to use the wear patterns of a card to improve OCR algorithm performance. The OCR application may apply a linear classifier and then a nonlinear classifier to improve the performance and the accuracy of the OCR algorithm. The OCR application uses the known digit patterns used by typical credit and debit cards to improve the accuracy of the OCR algorithm. | 03-03-2016 |
20160086056 | SYSTEMS AND METHODS FOR RECOGNIZING ALPHANUMERIC CHARACTERS - Systems and methods for recognizing alphanumeric characters are described. In one implementation, the method for recognizing alphanumeric characters comprises receiving features for each of the alphanumeric characters to be recognized. The features comprises at least one of number of lines, length of lines, number of arcs, length of arcs, number of loops, length of loops, number of junctions, junction positions, or total length of lines, arcs, and loops. Further, the method comprises creating a vector for each of the alphanumeric characters based on the features. Further, the method comprises comparing the vector with a reference vector obtained from a reference database. Further, the method comprises determining an array of probabilities for each of the alphanumeric characters based on the comparison. Further, the method comprises recognizing the alphanumeric characters based on the array of probabilities. | 03-24-2016 |
20160117551 | Methods for Automatic Structured Extraction of Data in OCR Documents Having Tabular Data - Methods to select and extract tabular data among the optical character recognition returned strings to automatically process documents, including documents containing academic transcripts. | 04-28-2016 |
20160132753 | NONPARAMETRIC MODEL FOR DETECTION OF SPATIALLY DIVERSE TEMPORAL PATTERNS - A computer-implemented method of generating a spatio-temporal pattern model for spatio-temporal pattern recognition includes receiving one or more training trajectories. Each of the training trajectories includes diverse data points that represent a spatio-temporal pattern. The received training trajectories define an area that is partitioned into one or more observed clusters, and a non-observed complementary cluster. The spatio-temporal pattern model is generated so as to include both of the observed clusters and the non-observed complementary cluster. | 05-12-2016 |