Check Image Field Randomization for Robust Document Model Training
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Solution Overview
Problem
Neural networks in financial software face overfitting issues due to limited and biased datasets, particularly in fields like date and bank routing numbers, leading to inaccurate predictions and potential financial losses.
Innovation Solution
Augment electronic documents with randomly generated synthetic sections and apply destructive data augmentation techniques to increase dataset diversity, using bounding box detection, random data generation, and image processing to enhance generalizability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are trained on limited and biased datasets, then training efficiency is improved, but model generalizability deteriorates
Solution Approach 1:
The system performs preliminary data augmentation by generating synthetic document images with randomized fields (dates, routing numbers, account numbers) before training begins. This pre-preparation of diverse training data allows the model to learn from varied examples without increasing the complexity of the training process itself, thus improving generalizability while maintaining training efficiency.
Solution Approach 2:
The system changes key parameters of the training data by randomly modifying specific fields (dates, routing numbers, account numbers) while keeping the overall document structure intact. This parameter variation approach enables the model to learn robust patterns without requiring complete retraining or complex architectural changes, balancing generalizability improvement with training efficiency.
2Measurement precision
If neural networks memorize training data patterns, then training accuracy is improved, but test data prediction accuracy deteriorates
Solution Approach 1:
The system applies local quality by selectively randomizing specific fields (dates, routing numbers, account numbers) while keeping other document features consistent. This allows the model to learn both the specific patterns it needs to memorize and the generalizable features, preventing overfitting to any single field while maintaining training accuracy on essential patterns.
Solution Approach 2:
The system introduces dynamics by making certain data fields variable and random during training (different dates, routing numbers, account numbers) rather than static. This dynamic variation prevents the model from memorizing fixed patterns and instead forces it to learn underlying relationships, improving test data prediction accuracy while maintaining reasonable training accuracy.
Data Source
AI summary
Disclosed herein are system, device, method and/or computer program product embodiments for training a machine learning model for processing an electronic document. To train the machine learning model, an embodiment may first collect electronic documents from a database. The embodiment may then detect a region of interest in each electronic document. The embodiment may then generate a random replacement image for each detected region of interest. The embodiment may then replace each detected region of interest with the corresponding generated random image. The embodiment may then generate a training set comprising the modified images. Finally, the embodiment may train the machine learning model using the generated training set.