Fast identification of text intensive pages from photographs
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.
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 to capture a plurality of partial lines of text;
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 as a part of training the classifier to distinguish between text documents and image documents.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. The method of claim 1 , including normalizing the size of a plurality of text snippets that include the respective text snippet.
8. 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.
9. The method of claim 7 , including normalizing the size of a plurality of image snippets that include the respective image snippet.
10. 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.
11. The method of claim 1 , wherein the classifier is a Neural Network.
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 to capture a plurality of partial lines of text;
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 as a part of training the classifier to distinguish between text documents and image documents.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. The computing device of claim 12 , including normalizing the size of a plurality of text snippets that include the respective text snippet.
19. 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.
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 to capture a plurality of partial lines of text;
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 as a part of training the classifier to distinguish between text documents and image documents.