Dynamic copyfitting parameter estimation

Patent metadata

Field	Value
Publication number	US-12223253-B2
Application number	US-202217984143-A
Country	US
Kind code	B2
Filing date	Nov 9, 2022
Priority date	Nov 9, 2022
Publication date	Feb 11, 2025
Grant date	Feb 11, 2025

Abstract

Embodiments are disclosed for real-time copyfitting using a shape of a content area and input text. A content area and an input text for performing copyfitting using a trained classifier is received. A number of remaining characters in the content area is computed in real-time using the input, the computing performed in response to receiving additional input text, wherein computing, in real-time, the number of remaining characters in the content area using the input text includes generating, by the trained classifier, a set of weights including a first set of one or more weights for the input text and a second set of one or more weights for the content area. The first set of one or more weights, the second set of one or more weights, the input text, and the additional input text, and a copyfitting parameter indicating a number of additional characters to be fitted into the content area are determined based on the content area. The copyfitting parameter and the number of remaining characters are presented in real-time.

First claim

We claim:

1. A method comprising:
receiving a content area of an electronic document and input text for copyfitting into the content area;
classifying, by a trained classifier, a shape of the content area;
generating a set of weights based on the classified shape of the content area, the set of weights including a first set of one or more weights for the input text and a second set of one or more weights for the content area;
determining copyfitting parameters for the content area based on content area features, the first set of one or more weights, the second set of one or more weights, and the input text, the copyfitting parameters including an indication of a number of remaining characters that can be inserted into the content area; and
presenting, in real-time, the copyfitting parameters.

2. The method of claim 1 , wherein the content area features include a minimum content area height, a maximum content area height, a content area width, and an area inside the content area.

3. The method of claim 2 , wherein input text includes a number of characters, an area covered by the input text, and a width of the input text, wherein each character has a font, a character height, and a character width.

4. The method of claim 3 , wherein determining the copyfitting parameters for the content area comprises:
applying the first set of one or more weights to the number of characters, a number of words in the input text, a width of the input text, an average width of a character, and a line height; and
applying the second set of one or more weights to the minimum content area height, the maximum content area height, the content area width, or the content area.

5. The method of claim 1 , wherein the first set of one or more weights for the input text comprises a weight for one or more of a number of characters in an input text, a number of words in the input text, an area covered by the input text, or a sum of the widths of characters in the input text.

6. The method of claim 1 , wherein the second set of one or more weights for the content area comprises a weight for one or more of a minimum width of a content area, a maximum width of the content area, a content area height, or area of the content area.

7. The method of claim 1 , wherein the copyfitting parameters further indicate a number of additional words or lines that can be inserted into the content area.

8. A system comprising:
a memory component; and
a processing device coupled to the memory component, the processing device to perform operations comprising:
receiving a content area of an electronic document and input text for copyfitting into the content area;
classifying, by a trained classifier, a shape of the content area;
generating a set of weights based on the classified shape of the content area, the set of weights including a first set of one or more weights for the input text and a second set of one or more weights for the content area;
determining copyfitting parameters for the content area based on content area features, the first set of one or more weights, the second set of one or more weights, and the input text, the copyfitting parameters including an indication of a number of remaining characters that can be inserted into the content area; and
presenting, in real-time, the copyfitting parameters.

9. The system of claim 8 , wherein the content area features include a minimum content area height, a maximum content area height, a content area width, and an area inside the content area.

10. The system of claim 9 , wherein input text includes a number of characters, an area covered by the input text, and a width of the input text, wherein each character has a font, a character height, and a character width.

11. The system of claim 10 , wherein the operation of determining the copyfitting parameters for the content area causes the processing device to perform operations comprising:
applying the first set of one or more weights to the number of characters, a number of words in the input text, a width of the input text, an average width of a character, and a line height; and
applying the second set of one or more weights to the minimum content area height, the maximum content area height, the content area width, or the content area.

12. The system of claim 8 , wherein the first set of one or more weights for the input text comprises a weight for one or more of a number of characters in an input text, a number of words in the input text, an area covered by the input text, or a sum of the widths of characters in the input text.

13. The system of claim 8 , wherein the second set of one or more weights for the content area comprises a weight for one or more of a minimum width of a content area, a maximum width of the content area, a content area height, or area of the content area.

14. The system of claim 8 , wherein the copyfitting parameters further indicate a number of additional words or lines that can be inserted into the content area.

15. A method comprising:
receiving training data that includes a shape of a content area, and input text, and a copyfitting label; and
training a classifier using the training data to:
classify a shape of the content areas to identify a matching content area shape,
generate a set of weights based on the classified shape of the content area, and
generate copyfitting parameters including a predicted number of remaining characters in the content area based on the input text, content area features, and the set of weights.

16. The method of claim 15 , wherein the copyfitting label indicates an overfit, an underfit, or a proper fit of the input text to the content area.

17. The method of claim 16 , wherein training the classifier using the training data comprises:
inserting the input text into the shape of content area;
generating the copyfitting parameters; and
comparing the copyfitting parameters with the copyfitting label.

18. The method of claim 17 , wherein the copyfitting label is a ground truth condition indicating an overfit or an underfit of the input text in the content area.

19. The method of claim 17 further comprising generating, using the comparison of the copyfitting parameters and the copyfitting label, a first set of weights for the input text by assigning a weight to a number of words in the input text, an area covered by the input text, and a sum of the widths of characters in the input text.

20. The method of claim 17 further comprising generating, using the comparison of the copyfitting parameters and the copyfitting label, a second set of one or more weights for the content area comprises a weight for one or more of a minimum width of a content area, a maximum width of the content area, a content area height, and an area of the content area.

Assignees

• Adobe Inc

Inventors

• Agarwal Rishav
• Hegde Vidisha Rama
• Gupta Vasu
• Jain Sanyam

Classifications

• G06F40/103 Primary

  Formatting, i.e. changing of presentation of documents (automatic justification G06F40/189; automatic line break hyphenation G06F40/191) · CPC title