# US 9,436,883 B2 · Collaborative text detection and recognition > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,436,883 B2 - Document ID: US9436883B2 - Status: Granted - Kind: B2 - Application number: 14/816,943 - Filed: 2015-08-03 - Published: 2015-11-26 - Granted: 2016-09-06 - Earliest claimed: 2013-12-12 - Applicants: A9.com, Inc. - Inventors: Xiaofan Lin; Adam Wiggen Kraft; Yu Lou; Douglas Ryan Gray; Colin Jon Taylor - Web page: https://patentgrep.com/patent/US9436883B2 ## CPC - G06V 30/413: Classification of content, e.g. text, photographs or tables - G06T 7/11: Region-based segmentation - G06V 30/142: using hand-held instruments; Constructional details of the instruments - G06V 30/224: of printed characters having additional code marks or containing code marks - G06F 2218/08: Feature extraction - G06V 30/10: Character recognition - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Various embodiments provide methods and systems for identifying text in an image by applying suitable text detection parameters in text detection. The suitable text detection parameters can be determined based on parameter metric feedback from one or more text identification subtasks, such as text detection, text recognition, preprocessing, character set mapping, pattern matching and validation. In some embodiments, the image can be defined into one or more image regions by performing glyph detection on the image. Text detection parameters applying to each of the one or more image regions can be adjusted based on measured one or more parameter metrics in the respective image region. ## Claims ### Claim 1 A computer-implemented method, comprising: receiving image data captured by a computing device; detecting text in the image data using at least one text detection parameter; determining that the text detected in the image data is recognized text; determining one or more parameter metrics of the image data; generating at least one modified text detection parameter based at least on the determined one or more parameter metrics and sensor data received from the computing device including ambient light, camera gain, and gyro-acceleration stability of the computing device; and applying the at least one modified text detection parameter to the image data. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: generating, in response to a quantity of the recognized text being less than a threshold amount within a predetermined time, a notification to disable a text identification function on the computing device. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, further comprising: causing a text identification function on the computing device to be placed in a disabled state in response to a quantity of the recognized text being less than a threshold amount within a predetermined time. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the one or more parameter metrics are measured in one or more text identification subtasks, the one or more text identification subtasks including text recognition, preprocessing, character set mapping, pattern matching, or validation. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising: determining one or more image regions in the image data, each of the one or more image regions covered by at least a portion of the text detected in the image data; determining the one or more parameter metrics of the image data that correspond to each of the one or more image regions; adjusting the at least one modified text detection parameter that applies to an image region of the one or more image regions, based on the determined one or more parameter metrics that correspond to the respective image region of the one or more image regions; and applying the adjusted at least one modified text detection parameter to the respective image region of the one or more image regions. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, wherein the one or more image regions include at least one image region from a plurality of image regions comprising: maximally stable extremal regions (MSERs), Harris-affine regions, Hessian-affine regions, Kadir-Brady saliency (KBS) regions, edge-based regions (EBR), and intensity extrema and salient regions. ### Claim 7 (depends on 5) The computer-implemented method of claim 5, wherein the one or more image regions in the image data are determined by performing glyph detection on the image data. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, further comprising: determining that one or more conditions are met, wherein the one or more conditions includes a condition that more than a threshold percentage of an image region, of the one or more image regions in the image data, is covered by a detected glyph. ### Claim 9 (depends on 7) The computer-implemented method of claim 7, further comprising: determining that one or more conditions are met; and assigning a confidence score to the recognized text, wherein the one or more conditions includes a condition that a percentage of detected glyph area covered by the recognized text that has at least a threshold confidence score is larger than a lower limit value. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, further comprising: determining that one or more conditions are met; and determining dominant word height of the text detected in the image data, wherein the one or more conditions includes a condition that the dominant word height is less than a threshold value. ### Claim 11 (depends on 1) The computer-implemented method of claim 1, further comprising: applying the at least one modified text detection parameter to one of subsequent image data received after the image data. ### Claim 12 A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to: receive image data captured by a computing device; detect text in the image data using at least one text detection parameter; determine that the text detected in the image data is recognized text; determine one or more parameter metrics of the image data; generate at least one modified text detection parameter based at least on the determined one or more parameter metrics and sensor data received from the computing device including ambient light, camera gain, and gyro-acceleration stability of the computing device; and apply the at least one modified text detection parameter to the image data. ### Claim 13 (depends on 12) The non-transitory computer-readable storage medium of claim 12, wherein the instructions when executed further cause the computing system to: generate, in response to a quantity of the recognized text being less than a threshold amount within a predetermined time, a notification to disable a text identification function on the computing device. ### Claim 14 (depends on 12) The non-transitory computer-readable storage medium of claim 12, wherein the instructions when executed further cause the computing system to: cause a text identification function on the computing device to be placed in a disabled state in response to a quantity of the recognized text being less than a threshold amount within a predetermined time. ### Claim 15 (depends on 12) The non-transitory computer-readable storage medium of claim 12, wherein the one or more parameter metrics are measured in one or more text identification subtasks, the one or more text identification subtasks including text recognition, preprocessing, character set mapping, pattern matching, or validation. ### Claim 16 (depends on 12) The non-transitory computer-readable storage medium of claim 12, wherein the instructions when executed further cause the computing system to: determine one or more image regions in the image data, each of the one or more image regions covered by at least a portion of the text detected in the image data; determine the one or more parameter metrics of the image data that correspond to each of the one or more image regions; adjust the at least one modified text detection parameter that applies to an image region of the one or more image regions, based on the determined one or more parameter metrics that correspond to the respective image region of the one or more image regions; and apply the adjusted at least one modified text detection parameter to the respective image region of the one or more image regions. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the one or more image regions in the image data are determined by performing glyph detection on the image data. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the instructions when executed further cause the computing system to: determine that one or more conditions are met, wherein the one or more conditions includes a condition that more than a threshold percentage of an image region, of the one or more image regions in the image data, is covered by a detected glyph. ### Claim 19 A system for identifying text in an image, comprising: at least one processor; and memory including instructions that, when executed by the at least one processor, cause the system to: receive image data captured by a computing device; detect text in the image data using at least one text detection parameter; determine that the text detected in the image data is recognized text; determine one or more parameter metrics of the image data; generate at least one modified text detection parameter based at least on the determined one or more parameter metrics and sensor data received from the computing device including ambient light, camera gain, and gyro-acceleration stability of the computing device; and apply the at least one modified text detection parameter to the image data. ### Claim 20 (depends on 19) The system of claim 19, wherein the instructions when executed further cause the system to: determine one or more image regions in the image data, each of the one or more image regions covered by at least a portion of the text detected in the image data; determine the one or more parameter metrics of the image data that correspond to each of the one or more image regions; adjust the at least one modified text detection parameter that applies to an image region of the one or more image regions, based on the determined one or more parameter metrics that correspond to the respective image region of the one or more image regions; and apply the adjusted at least one modified text detection parameter to the respective image region of the one or more image regions.