# US 2019/0318163 A1 · Fast identification of text intensive pages from photographs > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0318163 A1 - Document ID: US20190318163A1 - Status: Published application - Kind: A1 - Application number: 16/455,543 - Filed: 2019-06-27 - Published: 2019-10-17 - Earliest claimed: 2015-09-23 - Assignees: Evernote Corporation - Applicants: Evernote Corporation - Inventors: Alexander Pashintsev; Boris Gorbatov; Eugene Livshitz; Vitaly Glazkov - Web page: https://patentgrep.com/patent/US20190318163A1 ## CPC - G06V 30/413: Classification of content, e.g. text, photographs or tables - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G06T 7/60: Analysis of geometric attributes - G06V 30/414: Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Methods and systems for training a neural network to distinguish between text documents and image documents are described. A corpus of text and image documents is obtained. A page of a text document is scanned by shifting a text window to a plurality of locations. In accordance with a determination that the text in the window at a respective location meets text line criteria, the text in the window is stored as a respective text snippet. A plurality of image windows are superimposed over at least one page of an image document. In accordance with a determination that the content of a respective image window meets image criteria, content of the image window is stored as a respective image snippet. The respective text snippet and the respective image snippet are provided to a classifier. ## Claims ### Claim 1 A method of training a neural network to distinguish between text documents and image documents, comprising: obtaining a corpus of text and image documents; for at least one text document of the corpus of text and image documents: scanning at least one page of the at least one text document by shifting a text window to a plurality of locations on the at least one page of the at least one text document; determining whether text in the window at a respective location of the plurality of locations meets text line criteria; and in accordance with a determination that the text in the window at the respective location of the plurality of locations meets text line criteria, storing the text in the window as a respective text snippet; and for at least one image document of the corpus of text and image documents: superimposing a plurality of image windows over at least one page of the at least one image document; determining whether the content of a respective image window meets image criteria; and in accordance with a determination that the content of the respective image window meets the image criteria, storing the content of the respective image window as a respective image snippet; and providing the respective text snippet and the respective image snippet to a classifier. ### Claim 2 (depends on 1) The method of claim 1, wherein the text line criteria include first criteria that are met in accordance with a determination that a number of lines of text in the text in the window at the respective location of the plurality of locations is greater than a first predetermined number of lines of text. ### Claim 3 (depends on 2) The method of claim 2, wherein the text line criteria include second criteria that are met in accordance with a determination that a number of lines of text in the text in the window at the respective location of the plurality of locations is fewer than a second predetermined number of lines of text. ### Claim 4 (depends on 3) The method of claim 3, wherein the first predetermined number of lines of text is two and the second predetermined number of lines of text is four. ### Claim 5 (depends on 1) The method of claim 1, wherein the image criteria include criteria that are met in accordance with a determination that a portion of the content of the respective image window occupied by non-background content exceeds a predetermined threshold amount of non-background content. ### Claim 6 (depends on 1) The method of claim 1, wherein a first image window of the plurality of image windows has a first size that is different from a second size of a second image window of the plurality of image windows. ### Claim 7 (depends on 1) The method of claim 1, including normalizing the size of a plurality of text snippets that include the respective text snippet. ### Claim 8 (depends on 7) The method of claim 7, wherein normalizing the size of the plurality of text snippets includes converting each of the text snippets to a 32×32 pixel resolution. ### Claim 9 (depends on 7) The method of claim 7, including normalizing the size of a plurality of image snippets that include the respective image snippet. ### Claim 10 (depends on 9) The method of claim 9, wherein: the plurality of text snippets and the plurality of image snippets are added to a collection of training material; and the classifier is trained based on the collection of training material. ### Claim 11 (depends on 1) The method of claim 1, wherein the classifier is a Modified National Institute of Standards and Technology (MNIST) style Neural Network. ### Claim 12 A computing device for training a neural network to distinguish between text documents and image documents, comprising: one or more processors; memory; a display; and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs comprising instructions for: obtaining a corpus of text and image documents; for at least one text document of the corpus of text and image documents: scanning at least one page of the at least one text document by shifting a text window to a plurality of locations on the at least one page of the at least one text document; determining whether text in the window at a respective location of the plurality of locations meets text line criteria; and in accordance with a determination that the text in the window at the respective location of the plurality of locations meets text line criteria, storing the text in the window as a respective text snippet; and for at least one image document of the corpus of text and image documents: superimposing a plurality of image windows over at least one page of the at least one image document; determining whether the content of a respective image window meets image criteria; and in accordance with a determination that the content of the respective image window meets the image criteria, storing the content of the respective image window as a respective image snippet; and providing the respective text snippet and the respective image snippet to a classifier. ### Claim 13 (depends on 12) The computing device of claim 12, wherein the text line criteria include first criteria that are met in accordance with a determination that a number of lines of text in the text in the window at the respective location of the plurality of locations is greater than a first predetermined number of lines of text. ### Claim 14 (depends on 13) The computing device of claim 13, wherein the text line criteria include second criteria that are met in accordance with a determination that a number of lines of text in the text in the window at the respective location of the plurality of locations is fewer than a second predetermined number of lines of text. ### Claim 15 (depends on 14) The computing device of claim 14, wherein the first predetermined number of lines of text is two and the second predetermined number of lines of text is four. ### Claim 16 (depends on 12) The computing device of claim 12, wherein the image criteria include criteria that are met in accordance with a determination that a portion of the content of the respective image window occupied by non-background content exceeds a predetermined threshold amount of non-background content. ### Claim 17 (depends on 12) The computing device of claim 12, wherein a first image window of the plurality of image windows has a first size that is different from a second size of a second image window of the plurality of image windows. ### Claim 18 (depends on 12) The computing device of claim 12, including normalizing the size of a plurality of text snippets that include the respective text snippet. ### Claim 19 (depends on 18) The computing device of claim 18, wherein normalizing the size of the plurality of text snippets includes converting each of the text snippets to a 32×32 pixel resolution. ### Claim 20 A non-transitory computer readable medium containing software that trains a neural network to distinguish between text documents and image documents using a corpus of text and image documents, the software comprising executable code that: obtains a corpus of text and image documents; for at least one text document of the corpus of text and image documents: scans at least one page of the at least one text document by shifting a text window to a plurality of locations on the at least one page of the at least one text document; determines whether text in the window at a respective location of the plurality of locations meets text line criteria; and in accordance with a determination that the text in the window at the respective location of the plurality of locations meets text line criteria, stores the text in the window as a respective text snippet; and for at least one image document of the corpus of text and image documents: superimposes a plurality of image windows over at least one page of the at least one image document; determines whether the content of a respective image window meets image criteria; and in accordance with a determination that the content of the respective image window meets the image criteria, stores the content of the respective image window as a respective image snippet; and provides the respective text snippet and the respective image snippet to a classifier.