Convolutional Neural Network for Document Image Recognition
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Solution Overview
Problem
Traditional template-based systems for capturing and extracting information from images are inefficient and do not scale well due to the need for large numbers of templates and the challenges of handling image variations.
Innovation Solution
The use of deep learning techniques, specifically in fully convolutional neural networks, that are trained using dynamically generated data sets to recognize and extract various features from images, such as text, images, and signatures, without requiring specific image layouts or features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If template-based systems are used for information capture, then specific document types can be recognized, but the system requires large numbers of templates and cannot scale well
Solution Approach 1:
The patent replaces the mechanical/template-based recognition system with a deep learning neural network system. Instead of using multiple hand-crafted templates for different document types, the system uses a single neural network model that learns document representations from training data, automatically adapting to various document types without requiring manual template creation for each type.
Solution Approach 2:
The neural network system provides universality by handling multiple document types and information extraction tasks with a single model. The network can be trained on diverse document data and then applied to recognize and extract information from various document types, replacing the need for separate template-based systems for each document type.
2Ease of manufacture
If template-based systems are used, then known document layouts can be processed, but the system is not robust when dealing with image variations
Solution Approach 1:
The neural network system is dynamic in nature, automatically adapting to variations in document images during inference. The learned representations and decision boundaries in the network allow it to handle variations in lighting, perspective, font styles, and layout without requiring manual reconfiguration, unlike static template-based systems.
Solution Approach 2:
The system performs self-service by automatically learning from training data and self-adjusting to handle variations in input images. The neural network internally optimizes its own parameters during training to capture the essence of different document types and their variations, eliminating the need for manual template maintenance and updates.
3Manufacturing precision
If image preprocessing filters are applied, then some image quality issues can be addressed, but the filters are rarely effective due to variations in images
Solution Approach 1:
The patent replaces traditional image preprocessing filters with a neural network-based approach. Instead of relying on hand-crafted filters that fail to handle image variations, the system uses a neural network that learns robust features directly from training data, effectively replacing the need for multiple preprocessing stages with a single learned representation system.
4Productivity
If traditional approaches are used for information extraction, then text recognition can be performed, but non-textual information such as images, logos, and signatures cannot be extracted
Solution Approach 1:
The neural network system provides multi-functionality by simultaneously extracting multiple types of information (text, images, logos, signatures) from a single input document image. The network processes different information types through shared learned representations, enabling comprehensive information extraction in one unified system rather than requiring separate processing pipelines for each information type.
Data Source
AI summary
Systems, methods, and computer program products for image recognition in which instructions are executable by a processor to dynamically generate simulated documents and corresponding images, which are then used to train a fully convolutional neural network. A plurality of document components are provided, and the processor selects subsets of the document components. The document components in each subset are used to dynamically generate a corresponding simulated document and a simulated document image. The convolutional neural network processes the simulated document image to produce a recognition output. Information corresponding to the document components from which the image was generated is used as an expected output. The recognition output and expected output are compared, and weights of the convolutional neural network are adjusted based on the differences between them.