| 序号 | 专利名 | 申请号 | 申请日 | 公开（公告）号 | 公开（公告）日 | 发明人 |
|---|---|---|---|---|---|---|
| 841 | Method for identifying unrecognizable characters in optical character recognition machines | US360967 | 1989-06-02 | US4914709A | 1990-04-03 | Peter Rudak |
| A method for identifying a character which cannot be machine read so that the operator may observe and hopefully recognize the character in question. A bit-map video image of the unrecognized character(s) is inserted in the ASCII data line of neighboring characters to create an impression of the original line of text from the document. A data entry operator uses this information to enter the required correct character(s) via the keyboard or other means. This reject/reentry method allows for quick operator response, and minimizes data storage and transmission of video information. | ||||||
| 842 | System and method for improved optical character recognition for automated set-top box testing | US15338983 | 2016-10-31 | US09942543B2 | 2018-04-10 | Liam Friel |
| The present application provides a user configurable test system for set-top boxes (STB) and other consumer devices providing video output. In particular, it provides for a method of improving an Optical Character Recognition (OCR) process in such test systems. | ||||||
| 843 | OPTICAL CHARACTER RECOGNITION (OCR) ACCURACY BY COMBINING RESULTS ACROSS VIDEO FRAMES | US15218907 | 2016-07-25 | US20180025222A1 | 2018-01-25 | Vijay YELLAPRAGADA; Peijun CHIANG; Sreeneel K. MADDIKA |
| The present disclosure relates to optical character recognition using captured video. According to one embodiment, using a first image in stream of images depicting a document, the device extracts text data in a portion of the document depicted in the first image and determines a first confidence level regarding an accuracy of the extracted text data. If the first confidence level satisfies a threshold value, the device saves the extracted text data as recognized content of the source document. Otherwise, the device extracts the text data from the portion of the document as depicted in one or more second images in the stream and determines a second confidence level for the text data extracted from each second image until identifying one of the second images where the second confidence level associated with the text data extracted from the identified second image satisfies the threshold value. | ||||||
| 844 | METHOD AND SYSTEM FOR PREPARING TEXT IMAGES FOR OPTICAL-CHARACTER RECOGNITION | US15238350 | 2016-08-16 | US20180018774A1 | 2018-01-18 | Olga Arnoldovna Kacher; Ivan Germanovich Zagaynov; Vladimir Rybkin |
| The current document is directed to methods and systems that straighten in the text lines of text-containing digital images. Initial processing of a text-containing image identifies the outline of a text-containing page. Next, aggregations of symbols, including words and word fragments, are identified within the outlined page image. The centroids and inclination angles of the symbol aggregations are determined, allowing each symbol aggregation to be circumscribed by a closest-fitting rectangle oriented in conformance with the inclination angle determined for the circumscribed symbol aggregation. A model is constructed for the text-line curvature within the text image based on the circumscribed symbol aggregations and is refined using additional information extracted from the text image. The model, essentially an inclination-angle map, allows for assigning local displacements to pixels within the page image which are then used to straighten the text lines in the text image. | ||||||
| 845 | METHOD AND SYSTEM THAT DETERMINE THE SUITABILITY OF A DOCUMENT IMAGE FOR OPTICAL CHARACTER RECOGNITION AND OTHER IMAGE PROCESSING | US15165512 | 2016-05-26 | US20170293818A1 | 2017-10-12 | Ivan Germanovich Zagaynov; Vasily Vasilievich Loginov; Nikita Konstantinovich Orlov |
| The current document is directed to a computationally efficient method and system for assessing the suitability of a text-containing digital image for various types of computational image processing, including optical-character recognition. A text-containing digital image is evaluated by the disclosed methods and systems for sharpness or, in other words, for the absence of, or low levels of, noise, optical blur, and other defects and deficiencies. The sharpness-evaluation process uses computationally efficient steps, including convolution operations with small kernels to generate contour images and intensity-based evaluation of pixels within contour images for sharpness and proximity to intensity edges in order to estimate the sharpness of a text-containing digital image for image-processing purposes. | ||||||
| 846 | Hierarchical Information Extraction Using Document Segmentation and Optical Character Recognition Correction | US15620733 | 2017-06-12 | US20170277946A1 | 2017-09-28 | Jan Stadermann; Denis Jager; Uri Zernik |
| Systems, methods, and media for extracting and processing entity data included in an electronic document are provided herein. Methods may include executing one or more extractors to extract entity data within an electronic document based upon an extraction model for the document, selecting extracted entity data via one or more experts, each of the experts applying at least one business rule to organize at least a portion of the selected entity data into a desired format, and providing the organized entity data for use by an end user. | ||||||
| 847 | Techniques for distributed optical character recognition and distributed machine language translation | US14264327 | 2014-04-29 | US09514377B2 | 2016-12-06 | Alexander Jay Cuthbert; Peng Xu |
| A technique for selectively distributing OCR and/or machine language translation tasks between a mobile computing device and server(s) includes receiving, at the mobile computing device, an image of an object comprising a text. The mobile computing device can determine a degree of optical character recognition (OCR) complexity for obtaining the text from the image. Based on this degree of OCR complexity, the mobile computing device and/or the server(s) can perform OCR to obtain an OCR text. The mobile computing device can then determine a degree of translation complexity for translating the OCR text from its source language to a target language. Based on this degree of translation complexity, the mobile computing device and/or the server(s) can perform machine language translation of the OCR text from the source language to a target language to obtain a translated OCR text. The mobile computing device can then output the translated OCR text. | ||||||
| 848 | Techniques for distributed optical character recognition and distributed machine language translation | US14264296 | 2014-04-29 | US09514376B2 | 2016-12-06 | Alexander Jay Cuthbert; Peng Xu |
| A technique for selectively distributing OCR and/or machine language translation tasks between a mobile computing device and server(s) includes receiving, at the mobile computing device, an image of an object comprising a text. The mobile computing device can determine a degree of optical character recognition (OCR) complexity for obtaining the text from the image. Based on this degree of OCR complexity, the mobile computing device and/or the server(s) can perform OCR to obtain an OCR text. The mobile computing device can then determine a degree of translation complexity for translating the OCR text from its source language to a target language. Based on this degree of translation complexity, the mobile computing device and/or the server(s) can perform machine language translation of the OCR text from the source language to a target language to obtain a translated OCR text. The mobile computing device can then output the translated OCR text. | ||||||
| 849 | METHOD AND IMAGE PROCESSING APPARATUS FOR PERFORMING OPTICAL CHARACTER RECOGNITION (OCR) OF AN ARTICLE | US14746198 | 2015-06-22 | US20160259991A1 | 2016-09-08 | Tomson Ganapathiplackal GEORGE; Sudheesh Joseph |
| Embodiments of the present disclosure disclose a method for performing Optical Character Recognition (OCR) of an article. The method comprises acquiring an image of the article. The image of the article is scanned using predetermined scan settings. Then, textual regions of the scanned image of the article are identified. The OCR of the at least one of the textual regions is performed using predetermined OCR settings. One or more textual regions of the textual regions are marked upon determining an error in performing the OCR of the one or more textual regions. The OCR of the one or more textual regions is iterated as per one or more predefined OCR scanning parameters based on an OCR quality of the one or more textual regions upon marking the one or more textual regions. | ||||||
| 850 | Method and system for identifying anchors for fields using optical character recognition data | US13855933 | 2013-04-03 | US09396540B1 | 2016-07-19 | Steven Sampson |
| Identifying anchors for fields using optical character recognition data is described. A collection of characters is identified. The collection of characters includes a first set of characters at a first position relative to a first field in a first document and a second set of characters at a second position relative to the first field in the first document. The first set of characters is associated with a first word, and the second set of characters is associated with a second word. An anchor is created based on the collection of characters, wherein the anchor is at a third relative position to the first field in the first document. A second field is identified in a second document by identifying the anchor in the second document. | ||||||
| 851 | System and method for selecting segmentation parameters for optical character recognition | US13653948 | 2012-10-17 | US09317767B2 | 2016-04-19 | Ali Zadeh; John Petry |
| A computer-implemented method for selecting at least one segmentation parameter for optical character recognition is provided. The method can include receiving an image having a character string that includes one or more characters. The method can also include receiving a character string identifying each of the one or more characters. The method can also include automatically generating at least one segmentation parameter. The method can also include performing segmentation on the image having the character string using the at least one segmentation parameter. The method can also include determining if a resultant segmentation satisfies one or more criteria and if the resultant segmentation satisfies the one or more criteria, selecting the at least one segmentation parameter. | ||||||
| 852 | Efficient identification and correction of optical character recognition errors through learning in a multi-engine environment | US13619853 | 2012-09-14 | US09053350B1 | 2015-06-09 | Ahmad E. Abdulkader; Matthew R. Casey |
| OCR errors are identified and corrected through learning. An error probability estimator is trained using ground truths to learn error probability estimation. Multiple OCR engines process a text image, and convert it into texts. The error probability estimator compares the outcomes of the multiple OCR engines for mismatches, and determines an error probability for each of the mismatches. If the error probability of a mismatch exceeds an error probability threshold, a suspect is generated and grouped together with similar suspects in a cluster. A question for the cluster is generated and rendered to a human operator for answering. The answer from the human operator is then applied to all suspects in the cluster to correct OCR errors in the resulting text. The answer is also used to further train the error probability estimator. | ||||||
| 853 | Local scale, rotation and position invariant word detection for optical character recognition | US13734760 | 2013-01-04 | US09025877B2 | 2015-05-05 | Sri-Kaushik Pavani; Ekta Prashnani |
| A system and method using a text extraction application for identifying words with multiple orientations from an image are described. The text extraction application receives an input image, generates progressively blurred images, detects blobs in the blurred images, outputs ellipses over the blobs, detects a word in the input image, orients and normalizes a first version of the word, generates an inverted version of the word, performs OCR on the first version and the inverted version of the word, generates confidence scores for the first version and the inverted version of the word and outputs text associated with the word. | ||||||
| 854 | OPTICAL CHARACTER RECOGNITION BY ITERATIVE RE-SEGMENTATION OF TEXT IMAGES USING HIGH-LEVEL CUES | US13480728 | 2012-05-25 | US20150055866A1 | 2015-02-26 | Mark Joseph Cummins; Alessandro Bissacco |
| Disclosed techniques include receiving an electronic image containing depictions of characters, segmenting at least some of the depictions of characters using a first segmentation technique to produce a first segmented portion, and performing a first character recognition on the first segmented portion to determine a first sequence of characters. The techniques also include determining, based on the performing the first character recognition, that the first sequence of characters does not match the depictions of characters. The techniques further include segmenting at least some of the depictions of characters using a second segmentation technique, based on the determining, to produce a second segmented portion, and performing a second character recognition on at least a portion of the second segmented portion to produce a second sequence of characters. The techniques also include outputting a third sequence of characters based on at least part of the second sequence of characters. | ||||||
| 855 | Optical character recognition (OCR) engines having confidence values for text types | US12954865 | 2010-11-27 | US08452099B2 | 2013-05-28 | Prakash Reddy |
| An image of a known text sample having a text type is generated. The image of the known text sample is input into each OCR engine of a number of OCR engines. Output text corresponding to the image of the known text sample is received from each OCR engine. For each OCR engine, the output text received from the OCR engine is compared with the known text sample, to determine a confidence value of the OCR engine for the text type of the known text sample. | ||||||
| 856 | SYSTEM AND METHOD FOR USING OPTICAL CHARACTER RECOGNITION TO EVALUATE STUDENT WORKSHEETS | US13012359 | 2011-01-24 | US20120189999A1 | 2012-07-26 | Todd Maurice Uthman; Frank Porter; Adam D. Ledgerwood |
| A system and method for evaluating worksheets are provided. The method includes accessing by a processor a digitally stored image of a worksheet that was administered to a student, wherein the worksheet includes the presentation of at least one problem for the student to respond to and each problem presentation includes an answer region that the student manually, mechanically, or electronically marked with an answer to the problem using a variety of alphanumeric characters. The method further includes generating expected answer data that provides an expected answer for each answer region; locating each answer region in the image; extracting from each located answer region the student's marks; processing the student's marks extracted from each answer region with character recognition processing; and storing the processed marks as student answer data. The student answer data is evaluated by comparing the student answer data with the expected answer data and generating corresponding evaluation data. | ||||||
| 857 | Gesture processing with low resolution images with high resolution processing for optical character recognition for a reading machine | US12618858 | 2009-11-16 | US08150107B2 | 2012-04-03 | Raymond C. Kurzweil; Paul Albrecht; James Gashel; Lucy Gibson; Lev Lvovsky |
| A portable reading machine that operates in several modes and performs image preprocessing to prior to optical character recognition. The portable reading machine receives a low resolution image and a high resolution image of a scene and processing the low resolution image to recognize a user-initiated gesture using a gesturing item that indicates a command from the user to the reading machine and the high resolution image to recognize text in the image of the scene, according to the command from the user to the machine. | ||||||
| 858 | Method for optimized camera position finding for system with optical character recognition | US12963651 | 2010-12-09 | US08135217B2 | 2012-03-13 | Cuneyt Goktekin; Oliver Tenchio |
| The present invention relates to a method for aligning a camera sensor to significant data which is text or barcode data to be recognized comprising the steps of:—capturing an image of the significant data by means of the camera sensor; —detecting a predominant alignment line of the significant data and detecting an angle thereof in relation to a horizontal line of the captured image; —determining image sections within the edge and line enhanced image which contain most likely significant data lines; —selecting a representative image section out of the determined image sections which is aligned with the predominant alignment line; —capturing a following image of the significant data; tracking the representative image section and determining the predominant alignment line out of the representative image section to achieve a fast calculation and audio or tactile feedback of the alignment quality to the user. | ||||||
| 859 | Method For Optimized Camera Position Finding For System With Optical Character Recognition | US12963651 | 2010-12-09 | US20110181735A1 | 2011-07-28 | Cuneyt Goktekin; Oliver Tenchio |
| The present invention relates to a method for aligning a camera sensor to significant data which is text or barcode data to be recognized comprising the steps of:—capturing an image of the significant data by means of the camera sensor; —detecting a predominant alignment line of the significant data and detecting an angle thereof in relation to a horizontal line of the captured image; —determining image sections within the edge and line enhanced image which contain most likely significant data lines; —selecting a representative image section out of the determined image sections which is aligned with the predominant alignment line; —capturing a following image of the significant data; tracking the representative image section and determining the predominant alignment line out of the representative image section to achieve a fast calculation and audio or tactile feedback of the alignment quality to the user. | ||||||
| 860 | Reducing processing latency in optical character recognition for portable reading machine | US11097448 | 2005-04-01 | US07629989B2 | 2009-12-08 | Raymond C. Kurzweil; Paul Albrecht; Lucy Gibson |
| A portable reading device includes a computing device and a computer readable medium storing a computer program product to receive an image and select a section of the image to process. The product processes the section of the image with a first process and when the first process is finished processing the section of the image, process a result of the first process with a second process. While the second process is processing, repeats the first process on another section of the image. | ||||||
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