OCR Technology
- OCR Technology: Server Configuration
This article details the server configuration required to effectively implement Optical Character Recognition (OCR) technology within our infrastructure. It is aimed at newcomers to the system and will cover hardware, software, and necessary dependencies. Understanding these configurations is crucial for successful document processing and data extraction.
Introduction to OCR
Optical Character Recognition (OCR) is the process of converting images of text into machine-readable text data. This is vital for digitizing documents, automating data entry, and improving searchability. The performance of OCR is heavily dependent on the underlying server infrastructure. We utilize a distributed architecture to handle large volumes of documents, ensuring scalability and reliability. This guide focuses on the core components and configuration required for each server role. For more information on our overall data flow, please see Data Processing Pipeline.
Hardware Requirements
The following table outlines the recommended hardware specifications for the OCR servers. These specifications are based on our testing with typical document volumes and complexity. Different document types may necessitate adjustments. Refer to Performance Benchmarking for specific scenarios.
| Component | Minimum Specification | Recommended Specification | Optimal Specification |
|---|---|---|---|
| CPU | Intel Xeon E5-2620 v4 (6 cores) | Intel Xeon Gold 6248R (24 cores) | Dual Intel Xeon Platinum 8380 (40 cores) |
| RAM | 16 GB DDR4 ECC | 64 GB DDR4 ECC | 128 GB DDR4 ECC |
| Storage | 500 GB SSD | 1 TB NVMe SSD | 2 TB NVMe SSD (RAID 1) |
| Network | 1 Gbps Ethernet | 10 Gbps Ethernet | 10 Gbps Ethernet (Bonded) |
| GPU (Optional, for accelerated OCR) | N/A | NVIDIA Tesla T4 | NVIDIA A100 |
The choice of GPU is significant if you intend to leverage GPU-accelerated OCR engines like Tesseract with CUDA support (see GPU Acceleration). Storage speed is also crucial as OCR processes involve significant I/O operations. Consider using a dedicated storage network (SAN) for larger deployments; see Storage Area Network Configuration.
Software Stack
The OCR servers utilize a Linux-based operating system, specifically Ubuntu Server 22.04 LTS. This provides a stable and secure platform for our software stack. The core components include:
- Operating System: Ubuntu Server 22.04 LTS
- OCR Engine: Tesseract OCR 5.x
- Image Processing Libraries: OpenCV 4.x, ImageMagick 7.x
- Programming Language: Python 3.9 (with relevant libraries like Pillow, pytesseract)
- Message Queue: RabbitMQ 3.9 (for task distribution)
- Database: PostgreSQL 14 (for metadata storage)
- Web Server (for API access): Nginx 1.22
These components are managed using Ansible for automated deployment and configuration. Detailed installation instructions can be found on the Software Installation Guide page. It is vital to keep all software components updated to benefit from security patches and performance improvements. See Security Updates and Patching for our update schedule.
Network Configuration
Proper network configuration is essential for communication between the OCR servers, the message queue, and the database. The following table summarizes the required ports and services.
| Service | Port | Protocol | Description |
|---|---|---|---|
| SSH | 22 | TCP | Remote server access |
| RabbitMQ | 5672 | TCP | Message queue communication |
| PostgreSQL | 5432 | TCP | Database access |
| Nginx | 80/443 | TCP | API access (HTTP/HTTPS) |
| Tesseract OCR | N/A (typically accessed via Python scripts) | N/A | OCR processing |
Firewall rules must be configured to allow traffic on these ports. We use UFW (Uncomplicated Firewall) for firewall management. DNS resolution must be correctly configured to ensure all servers can communicate with each other by hostname. See DNS Configuration for details. Load balancing may be required for high-availability API access; see Load Balancer Configuration.
Performance Tuning
Optimizing OCR performance requires careful tuning of both the software and hardware. Key areas to focus on include:
- Tesseract Configuration: Adjusting Tesseract's parameters (e.g., page segmentation mode, OCR engine mode) can significantly impact accuracy and speed. See Tesseract Configuration Options.
- Image Pre-processing: Applying appropriate image pre-processing techniques (e.g., noise reduction, deskewing, binarization) can improve OCR accuracy. Utilize OpenCV and ImageMagick for these tasks; see Image Preprocessing Techniques.
- Resource Allocation: Monitoring CPU, RAM, and I/O usage to identify bottlenecks and adjust resource allocation accordingly. We use Prometheus and Grafana for monitoring.
- Parallel Processing: Leveraging multi-core processors and distributed processing to handle large volumes of documents concurrently.
The following table outlines some common Tesseract configuration parameters for performance tuning:
| Parameter | Description | Recommended Value |
|---|---|---|
| --psm | Page segmentation mode | 3 (Fully automatic page segmentation, but no OSD) |
| --oem | OCR engine mode | 1 (Neural nets LSTM engine only) |
| --tessdata-dir | Path to tessdata directory | /usr/share/tesseract-ocr/tessdata/ |
Regularly review Performance Monitoring Dashboard to identify and address performance issues.
Troubleshooting
Common issues encountered with OCR servers include:
- OCR Accuracy Issues: Poor image quality, incorrect language settings, or inappropriate Tesseract configuration.
- Performance Bottlenecks: High CPU usage, I/O limitations, or network congestion.
- Software Errors: Errors in Python scripts, database connection problems, or RabbitMQ failures.
Refer to the Troubleshooting Guide for detailed instructions on resolving these issues. The Logs Analysis page details how to interpret server logs for diagnostic purposes. For critical issues, contact the On-Call Support Team.
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️