Image-to-text recognition for sequences of images
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Solution Overview
Problem
Conventional image-to-text recognition systems are limited in processing sequences of images effectively, particularly when capturing words or symbols that cannot be fully represented in a single digital photo, leading to inefficiencies in translating or processing such content.
Innovation Solution
A system that captures a sequence of frames, parses image data into sub-images, identifies objects, generates meta data, and uses a natural language processing engine to convert the data into a natural language expression, incorporating positional information and language libraries for accurate translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single digital photo is processed for image-to-text recognition, then the processing is simple and fast, but the system cannot effectively capture words or symbols that span multiple frames or cannot be fully represented in a single frame
Solution Approach 1:
The system segments the image processing task by dividing a sequence of images into individual frames, then processing each frame separately through parsing, object identification, and text recognition. This allows the system to handle complex multi-frame content while maintaining efficient single-frame processing capabilities.
Solution Approach 2:
The system merges results from multiple frame processing operations by combining identified objects and text across frames, using metadata and positional information to integrate findings from individual frame analysis into a comprehensive sequence-level recognition result.
2Measurement precision
If multiple frames are captured and processed for image-to-text recognition, then the system can accurately identify objects and text across sequences, but the processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by capturing and storing metadata for each frame during the imaging process, including positional information and frame identifiers. This preliminary organization of data simplifies subsequent processing steps by having structured information ready before the actual recognition and comparison operations begin.
Solution Approach 2:
The system uses an intermediary metadata structure that bridges raw image data and final recognition results. This metadata layer includes positional information, frame sequences, and object identifiers that mediate between the complex multi-frame input and the simplified output, managing complexity by introducing an organized intermediate representation.
3Loss of information
If frame-to-frame comparison is performed to obtain positional information, then the system can track objects across sequences, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential positional information and key object identifiers from each frame for comparison purposes, rather than processing entire frames. This extraction of critical data elements reduces the computational burden of frame-to-frame comparison while maintaining the ability to track objects and retain positional information across sequences.
Data Source
AI summary
Systems and methods relate generally to image-to-text recognition. In an example thereof, a device captures an image and generates image data and associated meta data. A parser parses the image data and the meta data into sub-image data and sub-meta data, respectively. A recognizer renders the sub-image data into strings for one or more identified objects in the sub-image data. A mapper-controller maps the sub-image data in association with the sub-meta data to generate a data structure having positional information with respect to the one or more identified objects. A dictionary store is accessed by a natural language processing engine responsive to the strings. The natural language processing engine converts the strings into a natural language expression for the strings and the positional information obtained from the data structure.