Transforming Data Extraction with AI-Powered Automation

To identify the optimal model for license field detection, multiple YOLO (You Only Look Once) variants were tested, including YOLO8 (nano and small) and YOLO11 (nano and medium). The goal was to find the right balance between detection accuracy and inference speed, critical for real-time document processing.

Training was conducted using a synthetic dataset of 600 driver’s license images sourced from the Roboflow dataset. To simulate real-world variability, the dataset underwent dynamic augmentation, introducing changes in lighting, orientation, and background noise. The data was then divided into three subsets: 70% for training, 20% for validation, and 10% for testing.

Over the course of 80 training epochs, performance was continuously monitored to track improvements and spot potential issues early. Hyperparameters were kept lean to enable faster iteration cycles and focus efforts on observable performance gains across models. This approach helped benchmark each variant under consistent conditions and build confidence in the most effective configurations for deployment.

Performance Visualizations

Two key outputs best represent the model’s performance - one showing classification accuracy across fields, and the other demonstrating how those results translate visually.

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This chart highlights how accurately the YOLO11m model classified each license field. Most predictions fall along the diagonal, indicating consistently correct identification across categories like name, address, and ID number. Minor overlap appears between similar fields, such as first and last names - expected due to their structural and positional similarities. Overall, the matrix reflects strong per-class accuracy and low rates of misclassification.

In this example, the model accurately places bounding boxes on a driver’s license, identifying key fields with precision. Despite layout variation and synthetic noise, the output demonstrates clear localization of names, dates, and other relevant fields - reinforcing the model’s ability to generalize beyond the training set.

Optical Character Recognition (OCR)

To evaluate text extraction performance, four OCR libraries were tested side by side: Tesseract, EasyOCR, GOT_OCR, and PaddleOCR. Each was assessed based on its accuracy across different license field types and its ability to process data efficiently. The goal was to determine which engine could best handle structured documents with speed and precision.

Alongside raw image testing, preprocessing techniques were introduced - specifically OTSU binarization - to analyze their impact on output quality. In some cases, preprocessing improved accuracy, especially for lower-quality images. However, results also highlighted the need for fine-tuned preprocessing strategies if the solution were to be scaled for production. These insights helped shape recommendations for future implementation and model tuning.

Performance Metrics

To evaluate the effectiveness of each OCR library, multiple dimensions were assessed - including accuracy at the character level and processing speed across document types.

This chart presents the Character Error Rate (CER) for each OCR model across different field types. GOT_OCR consistently delivered the lowest error rates - especially on structured fields like names, ID numbers, and dates - making it the most accurate library tested. In contrast, other engines showed higher error rates in complex or abbreviated fields like class codes.

Levenshtein Ratio Testing was used alongside CER to measure how closely the extracted text matched ground truth. This reinforced GOT_OCR’s lead, with high similarity ratios on most fields. Sequence Ratio Evaluation, which focused on multi-line fields such as addresses, highlighted how formatting complexity affected performance - especially for models not optimized for paragraph-style content.}

‍Inference time was also critical for real-time use cases. As shown in this chart, EasyOCR (GPU version) outperformed all others in speed, offering the fastest processing times across categories. However, this came with slightly reduced accuracy compared to GOT_OCR.

Together, these results reveal a clear trade-off between speed and precision. GOT_OCR is ideal when accuracy is the priority (e.g., compliance-related fields), while EasyOCR is well-suited for high-throughput, time-sensitive workflows. These insights guided the decision to consider hybrid OCR strategies — using faster engines for initial extraction and slower, more accurate ones for post-validation or critical fields.

Evaluation and Insights

The comprehensive testing process generated detailed performance data that shaped the understanding of each approach’s strengths and limitations. Confusion matrices offered a clear view of detection accuracy across the different YOLO variants, helping identify which models performed best under specific conditions.

OCR performance varied considerably depending on both the type of data field and the quality of the input image. Fields with complex structures or poor visual clarity introduced higher error rates, revealing the limitations of general-purpose OCR engines when applied to structured documents like driver’s licenses.

Recommendations for Future Work

The analysis provided specific, data-driven recommendations for production development:

1. Dataset Enhancement: Expand training data with greater diversity in license types, lighting conditions, and text-background contrast variations
2. Preprocessing Optimization: Fine-tune image preprocessing techniques for specific field types and quality conditions
3. Hybrid OCR Architecture: Consider combining multiple OCR approaches - using fast libraries for initial processing and accurate libraries for quality refinement