Text Wrap Detection Using Image Segmentation and Machine Learning
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Solution Overview
Problem
Conventional techniques fail to accurately detect text wrap in electronic documents, especially when the document format is not compatible with the viewing or editing application, and distinguishing wrapped text from other text is particularly challenging due to the variety of shapes and lack of format information.
Innovation Solution
A computing device implements a text wrap module with a preprocessing module that segregates digital content into text and non-text groups, overlays graphical elements to filter out text, and uses a machine-learning based wrap detection module, including a region proposal network and detection network, to identify text wrap in digital images, even in untagged documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used to identify text wrap in compatible document formats, then text wrap can be identified through format information, but text wrap cannot be detected in incompatible or untagged document formats
Solution Approach 1:
The document is segmented into text regions and non-text regions using image processing techniques. The preprocessing module divides the document image into multiple regions, identifies text regions through OCR or text detection algorithms, and separates them from non-text regions such as images and graphics. This segmentation enables the system to analyze text wrap behavior in each region independently, making the detection applicable to any document format regardless of compatibility.
Solution Approach 2:
An intermediary image processing layer is introduced between the document input and text wrap detection. This intermediary converts various document formats into a standardized image representation, extracting visual features such as text boundaries, spacing patterns, and layout structures. The wrap detection module then operates on these extracted features rather than relying on format-specific information, enabling universal text wrap detection across incompatible formats.
2Adaptability or versatility
If text wrap detection is performed on documents with variety of digital content shapes, then comprehensive text wrap detection is achieved, but distinguishing wrapped text from other text becomes particularly challenging
Solution Approach 1:
The system applies local quality analysis by examining specific local features around each detected object. For each non-text region, the system analyzes the local text flow patterns, line direction, spacing, and alignment in the surrounding areas. By focusing on these local quality metrics rather than global document properties, the system can distinguish wrapped text from other text even in complex layouts with varied digital content shapes.
Solution Approach 2:
The preprocessing module performs preliminary actions by pre-identifying and marking all text regions and non-text regions before the wrap detection phase. It预先 creates a map of text boundaries, identifies potential wrap scenarios, and prepares feature extracts that highlight text wrap characteristics. This preliminary organization of information reduces the complexity of the subsequent detection task and improves the ability to distinguish wrapped text from other text elements.
Data Source
AI summary
In implementations of text wrap detection, one or more computing devices of a system implement a text wrap module for detecting text wrap around a component of digital content of a document. The document is preprocessed to segregate the digital content into a text group and a non-text group. Members of the text group are overlaid with a graphical element colored to provide a contrast between the graphical element and the component of the digital content. The document is converted to a digital image and a feature map of the digital image is generated. The feature map is further processed using machine learning and a detection indication is output. The detection indication may indicate that text wrap is detected around a member of the text group, a member of the non-text group, or that no text wrap is detected.