Method and apparatus employing font size determination for resolution-independent rendered text for electronic documents

Patent metadata

Field	Value
Publication number	US-12586397-B2
Application number	US-202418405490-A
Country	US
Kind code	B2
Filing date	Jan 5, 2024
Priority date	Jan 5, 2024
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

Method and apparatus for determining font point size in bitmapped text does not rely on accuracy of an optical character recognition (OCR) engine, or on generation of heuristics (e.g. assumption of certain amounts of different types of text, such as capital, lowercase, ascending, descending) to determine a likely font size. A deep learning model for determining text size is based on extraction of features from existing text to obtain a more general solution.

First claim

What is claimed is:

1 . A method comprising:
a) responsive to receipt of a bitmap of a text line, performing a vertical histogram of pixels in the text line; b) scaling the bitmap of the text line to create an image; c) generating a horizontal histogram of the image to create an original feature vector; d) applying a transform to the image to create a further image; e) generating a horizontal histogram of the further image to create a further feature vector; f) concatenating the original feature vector with the further feature vector to create an output feature vector which is input to a deep learning model; g) applying the output feature vector to obtain a normalized point size; h) responsive to document parameters, determining a target resolution; i) responsive to h), determining a scaling factor; and j) responsive to i), determining a target point size.

2 . The method of claim 1 , further comprising removing, from the vertical histogram, lines with zero pixel values.

3 . The method of claim 1 , further comprising repeating d) and e) to generate a plurality of further feature vectors, and wherein f) comprises concatenating all of the further feature vectors with the original feature vector to create the output feature vector.

4 . The method of claim 1 , wherein the target point size is calculated as follows:

S
T

=

S
N

*

(

R
G

R
T

)

SF

where
S T is the target point size;
S N is the normalized point size;
R G is a ground truth resolution;
R T is the target resolution; and
SF is the scale factor.

5 . The method of claim 1 , wherein the transform comprises a Gabor filter.

6 . The method of claim 3 , wherein repeating d) comprises applying a plurality of Gabor filters to generate the plurality of further feature vectors.

7 . The method of claim 1 , wherein the deep learning model comprises a model selected from the group consisting of convolutional neural networks (CNN), deep convolutional neural networks (DCNN), and Gabor Convolutional Networks (GCN).

8 . The method of claim 1 , further comprising training the deep learning model using bitmap text of known point size.

9 . The method of claim 8 , further comprising rendering the bitmap text at a ground truth resolution and scaling the bitmap text to a target scale height.

10 . The method of claim 9 , further comprising scaling a known point size of the bitmap text based on a ratio of a height of a bounding box for a line of the bitmap text to the target scale height.

11 . An apparatus comprising at least one processor and at least one non-transitory memory storing instructions which, when executed by the at least one processor, perform a method comprising:
a) responsive to receipt of a bitmap of a text line, performing a vertical histogram of pixels in the text line; b) scaling the bitmap of the text line to create an image; c) generating a horizontal histogram of the image to create an original feature vector; d) applying a transform to the image to create a further image; e) generating a horizontal histogram of the further image to create a further feature vector; f) concatenating the original feature vector with the further feature vector to create an output feature vector which is input to a deep learning model; g) applying the output feature vector to obtain a normalized point size; h) responsive to document parameters, determining a target resolution; i) responsive to h), determining a scaling factor; and j) responsive to i), determining a target point size.

12 . The apparatus of claim 11 , wherein the method further comprises removing from the vertical histogram, lines with zero pixel values.

13 . The apparatus of claim 11 , wherein the method further comprises repeating d) and e) to generate a plurality of further feature vectors, and wherein f) comprises concatenating all of the further feature vectors with the original feature vector to create the output feature vector.

14 . The apparatus of claim 11 , wherein the target point size is calculated as follows:

S
T

=

S
N

*

(

R
G

R
T

)

SF

where
S T is the target point size;
S N is the normalized point size;
R G is a ground truth resolution;
R T is the target resolution; and
SF is the scale factor.

15 . The apparatus of claim 11 , wherein the transform comprises a Gabor filter.

16 . The apparatus of claim 11 , wherein repeating d) comprises applying a plurality of Gabor filters to generate the plurality of further feature vectors.

17 . The apparatus of claim 11 , wherein the deep learning model comprises a model selected from the group consisting of convolutional neural networks (CNN), deep convolutional neural networks (DCNN), and Gabor Convolutional Networks (GCN).

18 . The apparatus of claim 11 , wherein the method further comprises training the deep learning model using bitmap text of known point size.

19 . The apparatus of claim 18 , wherein the method further comprises rendering the bitmap text at a ground truth resolution and scaling the bitmap text to a target scale height.

20 . The apparatus of claim 19 , wherein the method further comprises scaling a known point size of the bitmap text based on a ratio of a height of a bounding box for a line of the bitmap text to the target scale height.

Assignees

• Konica Minolta Business Solutions Usa Inc

Inventors

• Grams Jason

Classifications

• G06V10/82

  using neural networks · CPC title

• G06V30/19147 Primary

  Obtaining sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06V30/1448

  based on markings or identifiers characterising the document or the area · CPC title

• G06V30/18086 Primary

  by performing operations within image blocks or by using histograms · CPC title