# US 2014/0193029 A1 · Text Detection in Images of Graphical User Interfaces

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2014/0193029 A1
- Document ID: US20140193029A1
- Status: Published application
- Kind: A1
- Application number: 13/736,258
- Filed: 2013-01-08
- Published: 2014-07-10
- Earliest claimed: 2013-01-08
- Inventors: Natalia Vassilieva
- Web page: https://patentgrep.com/patent/US20140193029A1

## CPC

- G06V 30/15: {Cutting or merging image elements, e.g. region growing, watershed or clustering-based techniques}
- G06V 30/155: {Removing patterns interfering with the pattern to be recognised, such as ruled lines or underlines}
- G06V 30/10: Character recognition
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Systems and methods for text detection are provided. An image is received, and a set of connected components in the image are determined. For each connected component in the set, a bounding area is determined. A set of regions of the image are determined, based on the bounding area. Each region in the set of regions is classified and normalized based on the classification. The normalized set of regions is merged into a binary image.

## Claims

### Claim 1

A method of text detection, the method comprising:
receiving, by a computer, an input image;
performing edge detection on the input image;
generating an edge map based on the input image;
generating a binary edge map;
determining a set of connected components in the binary edge map;
for each connected component in the set of connected components, determining a bounding area;
determining a set of regions of the input image based on the bounding area;
classifying each region in the set of regions;
normalizing the set of regions based on the classification; and
merging the normalized set of regions.

### Claim 2 (depends on 1)

The method of claim 1, wherein the input image is an image of a graphical user interface.

### Claim 3 (depends on 1)

The method of claim 1, further comprising: removing long horizontal line and long vertical lines from the binary edge map.

### Claim 4 (depends on 1)

The method of claim 1, wherein the region is classified as one of a white-text region, a black-text region, and non-text region.

### Claim 5 (depends on 1)

The method of claim 1, wherein classification of each region is based on at least one of a variance of stroke width for white pixels, a variance of stroke width for black pixels, a ratio of white pixels to black pixels in the region, and a ratio of white pixels to black pixels along a border of the region.

### Claim 6 (depends on 1)

The method of claim 1, wherein normalizing comprises:
determining a classification of a region in the set of regions; and
inverting the pixels in the region based on the classification.

### Claim 7 (depends on 1)

The method of claim 1, wherein normalizing comprises:
determining a region in the set of regions is classified as a black-text region; and
inverting the pixels in the region.

### Claim 8 (depends on 1)

The method of claim 1, wherein each bounding area corresponds to a region of the input image.

### Claim 9 (depends on 1)

The method of claim 1, further comprising: for each region in the set of regions, generating a binary image using an adaptive threshold.

### Claim 10 (depends on 1)

The method of claim 1, wherein the bounding area is a bounding rectangle.

### Claim 11 (depends on 1)

The method of claim 1, further comprising:
determining a region in the set of regions is classified as a non-text region; and
filtering-out the region from the set of regions.

### Claim 12 (depends on 1)

The method of claim 1, wherein the binary edge map is generated using a global threshold.

### Claim 13

A non-transitory computer-readable medium storing a plurality of instructions to control a data processor text detection, the plurality of instructions comprising instructions that cause the data processor to:
receive an image of a graphical user interface (GUI);
perform edge detection on the GUI image;
generate an edge map based on the GUI image;
generate a binary edge map;
determine a set of connected components in the binary edge map;
for each connected component in the set of connected components, determine a bounding area;
determine a set of regions of the input image based on the bounding area;
classify each region in the set of regions;
normalize the set of regions based on the classification; and
merge the normalized set of regions into a binary image.

### Claim 14 (depends on 13)

The non-transitory computer-readable medium of claim 13, wherein the region is classified as one of a white-text region, a black-text region, and non-text region.

### Claim 15 (depends on 13)

The non-transitory computer-readable medium of claim 13, wherein classification of each region is based on at least one of a variance of stroke width for white pixels, a variance of stroke width for black pixels, a ratio of white pixels to black pixels in the region, and a ratio of white pixels to black pixels along a border of the region.

### Claim 16 (depends on 13)

The non-transitory computer-readable medium of claim 13, wherein the instructions that cause the data processor to normalize the set of regions comprise:
instructions that cause the data processor to determine a classification of a region in the set of regions; and
instructions that cause the data processor to invert the pixels in the region based on the classification.

### Claim 17 (depends on 13)

The non-transitory computer-readable medium of claim 13, wherein the instructions that cause the data processor to normalize the set of regions comprise:
instructions that cause the data processor to determine a region in the set of regions is classified as a black-text region; and
instructions that cause the data processor to invert the pixels in the region.

### Claim 18

A system for text detection, the system comprising:
a processor; and
a memory coupled to the processor;
wherein the processor is configured to:
receive an image of a graphical user interface (GUI);
determine a set of connected components in the GUI image;
for each connected component in the set of connected components, determine a bounding area;
determine a set of regions of the GUI image based on the bounding area;
classify each region in the set of regions;
determine a region in the set of regions is classified as a black-text region;
invert the pixels in the region; and
merge the normalized set of regions into a binary image.

### Claim 19 (depends on 18)

The system of claim 18, wherein classification of each region is based on at least one of a variance of stroke width for white pixels, a variance of stroke width for black pixels, a ratio of white pixels to black pixels in the region, and a ratio of white pixels to black pixels along a border of the region.

### Claim 20 (depends on 18)

The system of claim 18, wherein the region is classified as one of a white-text region, a black-text region, and non-text region.