AI Image Recognition with Letter-Sequence Prediction for Unique Names
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Solution Overview
Problem
Conventional image recognition software using AI/ML models struggles with accurately identifying unique and non-ubiquitous content due to insufficient training datasets lacking ground truth samples for such content.
Innovation Solution
A method involving a server that extracts an image portion using predefined coordinates and optical character recognition, followed by executing an image recognition protocol for the first letter and a neural network with a nodal data structure to identify subsequent letters based on probabilities, utilizing a nodal data structure with interconnected nodes representing letters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image recognition software uses AI/ML models trained on ground truth datasets, then common and ubiquitous content can be accurately identified, but unique and non-ubiquitous content cannot be properly recognized due to insufficient training samples
Solution Approach 1:
The patent introduces an intermediary component (language model or contextual analysis module) that bridges the gap between image recognition and unique content identification. This intermediary uses contextual information, linguistic patterns, and probabilistic models to compensate for the lack of ground truth training data for unique names, enabling accurate identification of non-ubiquitous content while maintaining recognition accuracy for common content
Solution Approach 2:
The system dynamically adjusts recognition parameters and thresholds based on the uniqueness and frequency of the content being identified. For unique names with insufficient training samples, the system modifies its approach by incorporating additional features, changing decision thresholds, or switching to alternative recognition methods, thereby adapting to different data availability scenarios
2Ease of manufacture
If training datasets include only ground truth data from previously submitted checks, then training is simplified, but the model cannot generalize to unique names that appear infrequently
Solution Approach 1:
The system performs preliminary actions by pre-processing and augmenting the training data to create synthetic or pseudo-ground truth samples for unique names. It also pre-trains models on broader datasets and then fine-tunes them on domain-specific data, preparing the model in advance to handle unique content scenarios without requiring extensive manual annotation of every possible unique name
Solution Approach 2:
The system implements feedback mechanisms where recognition results are continuously evaluated and used to improve future performance. When unique names are misidentified or when confidence is low, the system adjusts its parameters, incorporates additional contextual information, or flags cases for manual review, creating a feedback loop that progressively improves reliability for unique content while maintaining training simplicity
Data Source
AI summary
Disclosed are methods and systems for using artificial intelligence (AI) for image recognition by using predefined coordinates to extract a portion of a received image, the extracted portion comprising a word to be identified having at least a first letter and a second letter; executing an image recognition protocol to identify the first letter; when the server is unable to identify the second letter, the server executes an AI model having a nodal data structure to identify the second letter based upon the identified first letter, the nodal data structure comprising a set of nodes where each node represents a letter, each node connected to at least one other node, wherein connection of a first node to a second node corresponds to a probability that a letter corresponding to the second node is used in a word subsequent to a letter corresponding to the first node.