# US 9,436,882 B2 · Automated redaction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,436,882 B2 - Document ID: US9436882B2 - Status: Granted - Kind: B2 - Application number: 14/020,524 - Filed: 2013-09-06 - Published: 2015-03-12 - Granted: 2016-09-06 - Earliest claimed: 2013-09-06 - Assignees: Lighthouse Document Technologies, Inc. - Applicants: Lighthouse Document Technologies, Inc. - Inventors: Christopher Byron Dahl; Debora Noemi Motyka Jones; Kevin Patrick O'Neill; Geoffrey Alan David Belger; Vladas Walter Mazelis; Nathaniel Byington; Beau Hodges Holt; John Charles Olson - Web page: https://patentgrep.com/patent/US9436882B2 ## CPC - G06V 30/40: Document-oriented image-based pattern recognition - G06V 30/226: of cursive writing - G06V 30/10: Character recognition - G06V 30/43: {Editing text-bitmaps, e.g. alignment, spacing; Semantic analysis of bitmaps of text without OCR} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract In embodiments, one or more computer-readable media may have instructions stored thereon which, when executed by a processor of a computing device provide the computing device with a redaction module. The redaction module may be configured to receive a request to redact a selection of text from a document and identify instances of the text occurring within the document through an analysis of word coordinate information of an image of the document. The redaction module may further be configured to generate redaction information, including redaction coordinates, the redaction coordinates may be based on the word coordinate information associated with respective instances of the text occurring within the document. The redactions, when applied to the image in accordance with the redaction coordinates, may redact the respective instances of the text. Other embodiments may be described and/or claimed. ## Claims ### Claim 1 One or more non-transitory computer-readable media having instructions stored thereon which, when executed by a processor of a computing device, provide the computing device with a redaction module to: receive a request to redact a selection of a group of text from a document, wherein the group of text comprises one or more words; identify instances of the group of text occurring within the document, including for each instance of the group of text, word coordinate information of the one or more words of the instance, wherein the word coordinate information of the one or more words of the instance includes (x, y) coordinates of the one or more words; and generate redaction information for a redaction mask, including redaction coordinates, for each instance of the group of text, wherein the redaction coordinates of each redaction mask include (x, y) coordinates of the redaction mask, wherein generation of the (x, y) coordinates of a redaction mask is based at least in part on the (x, y) coordinates of the one or more words of the instance of the group of text to be redacted, wherein application of the redaction masks in accordance with the redaction coordinates of the redaction masks redacts the respective instances of the group of text, wherein a y-height of the mask is substantially equal to a height of a tallest letter within the respective instances of the group of text, wherein the height of the tallest letter is greater than heights of at least some of other letters within the respective instances of the group of text. ### Claim 2 (depends on 1) The one or more non-transitory computer-readable media of claim 1, wherein the group of text comprises a line of text. ### Claim 3 (depends on 1) The one or more non-transitory computer-readable media of claim 1, wherein application of the redaction masks in accordance with the redaction coordinates of the redaction masks redacts whitespace and punctuation occurring within the respective instances of the text. ### Claim 4 (depends on 1) The one or more non-transitory computer-readable media of claim 1, wherein the redaction module is to further identify groups of words within the document as instances of the text from the document by computation of respective Levenshtein distance values between the groups of words and the text, and a determination of whether each Levenshtein distance value is within a determined threshold. ### Claim 5 (depends on 4) The one or more non-transitory computer-readable media of claim 4, wherein the determined threshold is based upon a number of characters contained within the text. ### Claim 6 (depends on 4) The one or more non-transitory computer-readable media of claim 4, wherein computation and determination further comprise reduction of the Levenshtein distance value between a group of words and the text, on detection that the Levenshtein distance value between the group of words and the text is within a first threshold, wherein reduction of the Levenshtein distance between a group of words and the text is based on a correlation of characters that contribute to the Levenshtein distance value with one or more characters commonly misinterpreted by an optical character recognition, OCR process; and wherein determination further comprises identification of the group of words as an instance of the text when the reduced Levenshtein distance value is within a second threshold. ### Claim 7 (depends on 6) The one or more non-transitory computer-readable media of claim 6, wherein the first and second thresholds are based on a number of characters contained within the text. ### Claim 8 (depends on 1) The one or more non-transitory computer-readable media of claim 1, wherein to receive further comprises receipt of the selected group of text and the document. ### Claim 9 (depends on 8) The one or more non-transitory computer-readable media of claim 8, wherein the redaction module is to further generate an image of the document to be redacted when the request includes copies of the document to be redacted. ### Claim 10 (depends on 9) The one or more non-transitory computer-readable media of claim 9, wherein the word coordinate information is generated by an optical character recognition (OCR) process performed on the image of the document. ### Claim 11 (depends on 1) The one or more non-transitory computer-readable media of claim 1, wherein generation of the (x, y) coordinates of a redaction mask based at least in part on the (x, y) coordinates of the one or more words of the instance of the group of text to be redacted comprises selection of the y-coordinates of the redaction mask based at least in part on highest and lowest y-coordinates of the one or more words of the instance of the group of text to be redacted, and selection of the x-coordinates of the redaction mask based at least in part on leftmost and rightmost x-coordinates of the one or more words of the instance of the group of text to be redacted. ### Claim 12 A system for redacting documents, comprising: a processor; and a redaction module to be operated by the processor to: receive a request to redact a selection of a group of text from a document, wherein the group of text comprises one or more words; identify instances of the group of text occurring within the document, including for each instance of the group of text, word coordinate information of the one or more words of the instance, wherein the word coordinate information of the one or more words of the instance includes (x, y) coordinates of the one or more words; and generate redaction information for a redaction mask, including redaction coordinates, for each instance of the group of text, wherein the redaction coordinates of each redaction mask include (x, y) coordinates of the redaction mask, wherein generation of the (x, y) coordinates of a redaction mask is based at least in part on the (x, y) coordinates of the one or more words of the instance of the group of text to be redacted, wherein to apply the redaction masks in accordance with the redaction coordinates of the redaction mask redacts the respective instances of the group of text, wherein a y-height of the mask is substantially equal to a height of a tallest letter within the respective instances of the group of text, wherein the height of the tallest letter is greater than heights of at least some of other letters within the respective instances of the group of text. ### Claim 13 (depends on 12) The system of claim 12, wherein the group of text comprises a line of text. ### Claim 14 (depends on 12) The system of claim 12, wherein the redaction masks applied to an image in accordance with the redaction coordinates redact whitespace and punctuation occurring within the respective instances of the text. ### Claim 15 (depends on 12) The system of claim 12, wherein the redaction module is to further identify groups of words within the document as instances of the text from the document by computation of respective Levenshtein distance values between the groups of words and the text, and a determination of whether each Levenshtein distance value is within a determined threshold. ### Claim 16 (depends on 15) The system of claim 15, wherein the determined threshold is based upon a number of characters contained within the text. ### Claim 17 (depends on 15) The system of claim 15, wherein computation and determination further comprises reduction of the Levenshtein distance value between a group of words and the text, on detection that the Levenshtein distance value between the group of words and the text is within a first threshold, wherein reduction of the Levenshtein distance between a group of words and the text is based on a correlation of characters that contribute to the Levenshtein distance value with one or more characters commonly misinterpreted by an OCR process; and wherein determination further comprises identification of the group of words as an instance of the text when the reduced Levenshtein distance value is within a second threshold. ### Claim 18 (depends on 17) The system of claim 17, wherein the first and second thresholds are based on a number of characters contained within the text. ### Claim 19 (depends on 12) The system of claim 12, wherein to receive further comprises receipt of the selected text and the document. ### Claim 20 (depends on 19) The system of claim 19, wherein the redaction module is to further generate an image of the document to be redacted when the request includes copies of the document to be redacted. ### Claim 21 (depends on 12) The system of claim 12, wherein the word coordinate information is generated by an optical character recognition (OCR) process performed on an image. ### Claim 22 A computer-implemented method for redacting documents, comprising: receiving, by a redaction module of a computing device, a request to redact a selection of a group of text from a document, wherein the group of text comprises one or more words; identifying, by the redaction module, instances of the group of text occurring within the document, including for each instance of the group of text, word coordinate information of the one or more words of the instance, wherein the word coordinate information of the one or more words of the instance includes (x, y) coordinates of the one or more words; and generating, by the redaction module, redaction information for a redaction mask, including redaction coordinates, for each instance of the group of text, wherein the redaction coordinates of each redaction mask include (x, y) coordinates of the redaction mask, wherein generating the (x, y) coordinates of the redaction mask is based at least in part on the (x, y) coordinates of the one or more words of the instance of the group of text to be redacted, wherein applying the redaction masks, in accordance with the redaction coordinates of the redaction masks, redacts the respective instances of the group of text, wherein a y-height of the mask is substantially equal to a height of a tallest letter within the respective instances of the group of text, wherein the height of the tallest letter is greater than heights of at least some of other letters within the respective instances of the group of text. ### Claim 23 (depends on 22) The computer-implemented method of claim 22, wherein the group of text comprises a line of text. ### Claim 24 (depends on 22) The computer-implemented method of claim 22, wherein applying the redaction masks in accordance with the redaction coordinates of the redaction masks redacts whitespace and punctuation occurring within the respective instances of the group of text. ### Claim 25 (depends on 22) The computer-implemented method of claim 22, further comprising identifying groups of words within the document as instances of the text in the document, by the redaction module, by computing respective Levenshtein distance values between the groups of words and the text, and determining whether each Levenshtein distance value is within a determined threshold. ### Claim 26 (depends on 25) The computer-implemented method of claim 25, wherein the determined threshold is based upon a number of characters contained within the text. ### Claim 27 (depends on 25) The computer-implemented method of claim 25, wherein computing and determining further comprises: reducing the Levenshtein distance value between a group of words and the text, on detecting the Levenshtein distance value between the group of words and the text is within a first threshold wherein reducing the Levenshtein distance between a group of words and the text is based on a correlation of characters contributing to the Levenshtein distance value with one or more characters commonly misinterpreted by an optical character recognition, OCR, process; and wherein determining further comprises identifying the group of words as an instance of the group of text when the reduced Levenshtein distance value is within a second threshold. ### Claim 28 (depends on 27) The computer-implemented method of claim 27, wherein the first and second thresholds are based on a number of characters contained within the text. ### Claim 29 (depends on 22) The computer-implemented method of claim 22, wherein receiving comprises receive the selected group of text and the document. ### Claim 30 (depends on 29) The computer-implemented method of claim 29, further comprising generating an image of the document to be redacted when the request includes copies of the document to be redacted. ### Claim 31 (depends on 22) The computer implemented method of claim 22, wherein the word coordinate information is generated by an optical character recognition (OCR) process performed on the image.