Cross-Channel Message Transformation Using OCR Text Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to effectively transform electronic messages from one channel to another, particularly due to differences in format and content requirements, such as converting emails to SMS messages without losing important information.
Innovation Solution
A system and method that utilizes optical character recognition (OCR) and HTML parsing to extract and prioritize text from formatted messages, adjusting parameters like size and placement, and generates optimized messages for the target channel based on recipient actions and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text is extracted and prioritized from formatted messages using OCR and HTML parsing, then message transformation accuracy is improved, but processing time increases
Solution Approach 1:
The processing system segments text extraction into distinct modules: OCR processing for image-based text, HTML parsing for structured content, and prioritization algorithms. This segmentation allows each component to be optimized independently and processed in parallel where possible, improving overall efficiency while maintaining accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing messages to identify and extract text before full transformation is needed. Text prioritization is performed in advance based on message structure and content analysis, so that when transformation is required, the work is already partially completed, reducing actual processing time.
2Productivity
If message content is condensed and optimized for different channels, then user engagement is improved, but information completeness may deteriorate
Solution Approach 1:
The system applies local quality by differentiating which information elements to preserve based on their importance and the target channel requirements. Critical information such as call-to-action elements, offers, and key details are prioritized for preservation, while less critical formatting and redundant content are condensed or removed. This ensures information completeness for essential elements while achieving optimization for engagement.
Solution Approach 2:
The transformation system changes parameters such as message length, formatting style, and content density based on the target channel characteristics. For example, SMS messages receive different parameter adjustments compared to email transformations, allowing each channel to receive optimally formatted content while preserving core information through intelligent parameter selection.
3Measurement precision
If multiple text extraction methods (OCR and HTML parsing) are used, then extraction accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements universality by creating a multi-functional text extraction platform that handles both OCR and HTML parsing through a unified architecture. This universal system can process different message formats (images with text, HTML emails, plain text) through the same core extraction engine, reducing the need for separate specialized systems and managing complexity through standardized interfaces.
Solution Approach 2:
An intermediary layer is introduced between the various extraction methods and the prioritization logic. This intermediary standardizes the output from different extraction methods (OCR, HTML parsing) into a common format, making it easier to manage and process the results without dealing with the complexities of each individual extraction method directly in the prioritization algorithms.
Data Source
AI summary
Apparatuses, methods, and systems for transforming a message of a first channel to a message of a second channel. One method includes receiving, by a server, a first channel electronic message, wherein the first channel electronic message includes a formatted message that includes at least text, extracting and prioritizing the at least text of the formatted message of the first channel electronic message, including converting the at least text of the formatted message of the first channel electronic message into machine-encoded text, and prioritizing text of the machine-encoded text based on at least a size and placement of the text of the formatted message. The method further includes drafting, by the server, a set of second channel electronic messages based on the prioritized text, and electronically sending the set of second channel electronic messages to recipients.