Web Page Image Data Processing via AI Recognition
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and recognizing data from web page images, particularly in identifying container types, text information, and image elements, which hinders the automation of page building and maintenance.
Innovation Solution
A method and apparatus that annotate page images to generate specific image sets, which are then input into a trained image recognition model to extract data sets. These data sets are converted based on page template information to create a template data set, facilitating efficient page processing and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used for page building and data processing, then development control and customization are maintained, but processing efficiency is low and development costs are high
Solution Approach 1:
The patent replaces manual mechanical page building and data processing with an automated image recognition system. The system captures webpage images and uses AI models to automatically extract and structure data, substituting human manual operations with automated optical and computational processes.
Solution Approach 2:
The system enables self-service automated page processing by capturing webpage images and automatically generating structured data without human intervention. The image recognition model autonomously performs data extraction, container type identification, and template matching tasks.
2Extent of automation
If automated image recognition is implemented for page processing, then processing efficiency and automation are improved, but system complexity and model training requirements increase
Solution Approach 1:
The patent segments the complex page processing task into distinct components: image capture, container type recognition, text information extraction, image element detection, and template matching. Each component is handled by a specialized sub-model or processing module, making the overall system more manageable.
Solution Approach 2:
The image recognition model is designed with multi-functionality to handle various processing tasks including container type identification, text extraction, and element detection within a single unified system, reducing the need for multiple separate systems.
3Measurement precision
If multiple image sets are generated for different recognition tasks, then recognition precision is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing the captured image into multiple specialized image sets (first image set for container types, second for text, third for elements) before recognition. This preparation enables parallel processing by different sub-models, improving overall efficiency despite the additional processing steps.
Solution Approach 2:
The patent transforms a single image processing task into multiple parallel processing dimensions by creating separate image sets for different recognition objectives. Each image set is processed simultaneously by specialized sub-models, converting sequential processing into parallel operations.
Data Source
AI summary
Disclosed are a data processing method and apparatus. The method includes: annotating, in response to receiving a page image, the page image to generate image sets corresponding to annotated data, the image sets including a first image set for recognizing a container type, a second image set for recognizing text information, and a third image set for detecting an image element; inputting the image sets into a trained image recognition model to generate a container type data set corresponding to the first image set, a text data set corresponding to the second image set and an image element data set corresponding to the third image set; performing a conversion on the container type data set, the text data set and the image element data set based on template information of a page to generate a template data set corresponding to the page image, and uploading the template data set.