5 Steps to Prepare OCR Training Data in 2024: An In-Depth Guide

Preparing high-quality training data is essential for developing accurate optical character recognition (OCR) and intelligent character recognition (ICR) systems. This comprehensive guide will walk you through the key steps involved in curating effective datasets to train robust OCR models.

By following the best practices outlined below, you can streamline your model development process and deploy high-performing OCR solutions for document digitization.

The Continued Need for OCR Despite the Rise of Automated Data Extraction

OCR technology has been around for decades, helping organizations extract text data from scanned documents and images. However, interest in OCR is now falling as more companies switch to automated data extraction tools powered by machine learning algorithms (see Figure 1).

Figure 1. Google Trends data showing declining interest in OCR terms over time.

According to [one survey], over 65% of companies now use intelligent document processing (IDP) platforms enabled by AI and machine learning for data extraction. The advanced capabilities of these tools allow them to handle unstructured data better than traditional OCR software.

However, OCR still remains relevant. Most IDP solutions still incorporate OCR technology in some form to digitize scanned documents as a precursor to extraction.

Additionally, [X% of] organizations continue to use or plan to implement OCR, especially for paper-heavy processes. Some key benefits of OCR include:

• Cost-effectiveness – OCR software is cheaper compared to advanced AI tools.
• Better accuracy – OCR yields higher accuracy for certain use cases like formatted documents.
• Ease of adoption – The technology is mature and widely understood.

So while automated data extraction is gaining popularity, quality training data is still essential for developing accurate OCR systems.

Why You Need Robust Training Data for OCR

OCR models rely on machine learning algorithms to recognize text patterns in images and documents. Like any ML model, the accuracy of an OCR system depends largely on the quality and size of the training data used to build it.

Some key reasons why curating proper training datasets is crucial:

• It teaches the OCR model – The model learns to recognize different text styles, arrangements, and languages by analyzing annotated samples.
• It improves real-world performance – Diverse, real-world data allows the model to generalize better and have fewer errors on live documents.
• It reduces bias – Varied data ensures the model works for different use cases rather just the samples it trained on.
• It speeds up development – With robust data, you need less iterations to tune the model leading to faster deployment.

However, preparing datasets for OCR models involves some unique challenges:

• Collecting thousands of document images at scale
• Cleaning and preprocessing unstructured image data
• Annotating text accurately for model training
• Eliminating noise and distortions to improve readability

By following the steps outlined in this guide, you can overcome these challenges and create high-quality training data for your OCR systems.

Step 1: Define the Purpose and Scope of Your Dataset

The first step is to clearly define the intended purpose of your OCR model. This will determine the scope of data you need to collect.

Some questions to ask:

• What types of documents will your model need to process?
  • e.g. invoices, medical records, bank statements
• What variations do you need to account for?
  • e.g. font sizes, text arrangements, document quality
• What data points need to be extracted?
  • e.g. amounts, dates, names
• What languages or text styles must it recognize?
  • e.g. handwritten notes, all-caps text

For instance, an OCR model for extracting structured fields from sales invoices would need to be trained on invoice images with text in varied locations, formats, and languages.

On the other hand, a model for reading handwritten doctor’s prescriptions must see many samples of messy handwriting in different styles and ink colors.

Once your model’s purpose is clearly defined, data collection becomes much more targeted.

Step 2: Collect Relevant and Diverse Training Data

With a defined scope, the next step is gathering relevant training samples that cover all the data variations your OCR model needs to handle.

Some best practices for building a robust dataset:

• Utilize various data sources – Combine publicly available datasets with private enterprise documents and data sourced from fieldwork.
• Introduce diversity – Vary language, document types, quality, text size/style.
• Clean and filter – Remove low-quality samples with too much noise.
• Gather at scale – Collect 10K+ samples for industrial-grade models.
• Maintain consistency – Use consistent naming, formats, and organization across the dataset.
• Watch for bias – Ensure there are no skewed distributions or missing categories.

There are several techniques you can leverage to assemble training data at scale:

• Web scraping – Programmatically extract sample documents posted online
• Field data collection – Digitize physical documents through scanning or mobile apps
• Synthetic data generation – Use ML to automatically create realistic sample images
• Crowdsourcing – Distribute data collection tasks to a distributed workforce

Combining these techniques allows you to build large and varied training sets. You may also want to source pre-labeled public datasets like this one to supplement your private data.

Step 3: Annotate the Documents for Text Extraction

Once you have raw document images, the next step is labeling the text in them to "teach" your OCR system what to recognize and extract.

Figure 2. Example of annotating text fields in a document for OCR training.

As shown above, this annotation process involves:

• Drawing bounding boxes around relevant text snippets
• Tagging each box with the appropriate text label
• Producing the final annotated samples for model training

You can manually annotate documents using specialized tools like Doccano. However, for large datasets, it helps to use automated assistance like OCR pre-annotation to reduce the manual workload.

When annotating, ensure that:

• Every sample has comprehensive labels for all data points of interest.
• Annotations accurately reflect the ground truth text.
• The formatting and style of annotations is consistent across the dataset.

Proper annotation is vital for maximizing OCR accuracy. Poor labels with errors, inconsistencies, or omissions will cripple model performance.

Step 4: Split Your Dataset for Training and Evaluation

Once annotation is complete, split your dataset into three parts:

• Training set – Used to train the parameters of your OCR model. Typically 60-80% of total data.
• Validation set – Used during training to tune hyperparameters and evaluate ongoing progress. Usually 10-20% of data.
• Test set – Used after finalizing the model to assess real-world performance. Make up the remaining 10-20% of data.

Properly dividing your dataset helps avoid overfitting where your model works well only on the data it trained on. It also allows proper model selection, tuning, and testing before final deployment.

To split correctly:

• Allocate data randomly or based on a stratified sampling strategy.
• Maintain similar text styles, classes, and distributions across all sets.
• Keep your test set locked away until final model testing.

Step 5: Preprocess and Clean Your Data

The last step is preprocessing your images and text to:

• Improve quality for better OCR accuracy
• Standardize the data format for easier ingestion
• Address anomalies that could confuse the model

Some key preprocessing operations to consider:

• Noise reduction – Remove specks, blotches, lines
• Deskewing – Correct tilted documents
• Binarization – Convert to black & white
• Resizing – Standardize image dimensions
• Text normalization – Fix spelling variations

The right algorithms can enhance OCR data considerably. For instance, descreening a low-resolution scanned document makes the text far easier to recognize correctly.

Figure 3. An example of image preprocessing operations like deskewing applied to OCR data.

Ready to Implement Your OCR System?

By taking the right approach to curating and preparing training datasets, you can develop highly accurate OCR models for enterprise use.

The key is investing time upfront during data collection and preprocessing. This provides your machine learning algorithms with the best chance to learn to replicate human-level text recognition capabilities.

To recap, be sure to:

• Clearly define your OCR model‘s purpose and data needs
• Collect diverse and relevant document samples at scale
• Carefully annotate text snippets and data fields
• Split data into training and test sets
• Preprocess images to improve quality

Adhering to these data preparation best practices will lead to superior model performance and improved OCR accuracy on business documents.

For more guidance on training machine learning models, download our free AI Engineering Playbook:

Download Playbook

Additionally, view our curated list of leading data annotation services to scale up your dataset creation. Reach out if you need any help jumpstarting your next OCR project.