Optical character recognition (OCR) is a technology that enables the conversion of images containing text into machine-readable text. With recent advancements in deep learning and computer vision, OCR has become increasingly accurate and useful for automating document digitization workflows.
However, developing an accurate OCR system still requires properly prepared and annotated training data. As an expert in data extraction and document digitization with over 10 years of experience, I have helped numerous companies build custom OCR systems. In this comprehensive guide, I will share the key steps involved in preparing high-quality training data to develop robust OCR systems in 2024.
1. Define the Purpose and Scope
The first and most crucial step is to clearly define the purpose and scope of the OCR system you want to build. This will guide the entire data collection and annotation process.
Based on my experience, here are some key questions to consider:
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What types of documents do you want the OCR to handle? Scanned documents, photos, screenshots, handwritten notes, specific document types like invoices, ID cards etc.
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What formats do you need to support? JPEG, PNG images or PDF documents?
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What languages does it need to recognize? English only or multiple languages?
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Does it need to recognize only printed or also handwritten text? Handwritten text is much harder to recognize accurately.
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What industry or domain is the OCR for? It helps to tailor the system to a specific domain like healthcare, legal, financial services etc. as documents have industry-specific elements.
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What output do you need? Just raw text or structured text with bounding box coordinates, font styles, document structure etc.
According to a survey by CloudFactory, 63% of companies report unclear objectives as a top data labeling challenge. Defining the purpose and scope early on ensures you gather the right data to train the OCR for its intended use case.
For example, a mortgage company wanted to build an OCR to extract key fields from scanned mortgage application forms. We defined the scope to recognize handwritten and machine printed text in English, and output structured data with text, bounding boxes and field labels.
2. Collect Relevant Data
Once the scope is defined, the next step is to collect relevant images and documents to train the OCR system.
The diversity and quality of training data has a direct impact on the accuracy of the resulting OCR system. The data should cover various scenarios and use cases the OCR is likely to encounter during real-world usage.
Based on my experience building OCR training datasets, here are some best practices to follow:
Diversify Image and Text Features
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Include diverse fonts and styles – Gather text in different fonts (serif, sans-serif, script, decorative, etc.), font sizes, italic/bold styling, underline, letter spacing, etc.
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Vary text arrangements – Collect data with text arranged vertically, overlapping, curved, inverted, rotated, distorted perspectives and all possible orientations.
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Use complex backgrounds – Include images with text over busy backgrounds, low contrast, watermarks, creases, to improve robustness.
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Capture lighting variations – Have data with shadows, glares, dark and low-lit images to handle illumination changes.
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Include imperfections – Introduce scans with smudges, dust, dirt, stains, specks and other defects.
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Gather multi-lingual data – For multi-lingual OCR, ensure adequate text in each language script.
Include Real-World Variants
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Source from different eras – Include old historical documents with faded ink, yellowing etc.
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Vary scanning devices – Scan documents with different scanners, cameras, mobile phones under different settings.
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Capture various conditions – Have crumpled, folded, torn documents showing real-world wear and tear.
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Real-world noise – Introduce artifacts like punch holes, staple marks, creases, tape, coffee stains.
Include Corner Cases
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Challenging fonts – Incorporate artistic, calligraphic, distorted, styled fonts.
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Partial occlusion – Have samples with obstruction, masking and overlapping objects.
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Unusual arrangements – Add text set on odd angles, curves, shapes, circular arrangements.
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Low resolution – Include smaller, pixelated, compressed, blurred documents and screenshots.
According to recent research from Google, training on a more diverse dataset can improve OCR accuracy on challenging real-world documents by up to 18.5%.
There are several ways to collect OCR training data:
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Scan or photograph documents – In-house scanning of various document types using different devices.
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Leverage public datasets – Public datasets like MNIST digits, IAM Handwriting Database etc. can be used for baseline training.
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Synthetic data generation – Leverage data augmentation techniques to synthesize training images.
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Outsourcing – Outsource document scanning and OCR data collection to expert data partners to scale.
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Crowdsourcing – Leverage crowdsourcing platforms to distribute scanning and transcription tasks.
I recommend having at least 5,000 to 10,000 annotated training images for adequate accuracy, with more data needed for handwritten OCR.
3. Annotate the Images for Text Localization
Once suitable training images are collected, the next step is to annotate the text elements in each image using bounding boxes and transcription.
Proper annotation is crucial for training high-accuracy OCR models. For OCR, common annotation approaches are:
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Bounding box – Draw tight bounding boxes around each text line or paragraph. Fastest to annotate.
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Word polygon – Draw a polygon along the boundary of each word. Allows tight cropping of words.
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Character polygon – Outline each individual character precisely. More effort but provides detailed segmentation.
My recommendation is tightly bounded bounding boxes around each text line, as it provides the best balance of speed and accuracy for most use cases.
Here are some best practices I recommend for OCR image annotation:
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Annotate only the foreground text, ignoring the background.
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For illegible or obscured text, mark as "[illegible]" so the model can learn to recognize.
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Use consistent capitalization, spellings, punctuation in transcription.
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Have multiple annotators for consensus to catch errors – aim for 99%+ accuracy.
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Leverage annotation tools like Doccano, Labelbox, etc to speed up annotation.
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Set up annotation guidelines and quality checks.
Proper annotation is critical for OCR – budget 15-30 seconds per image for careful annotation depending on complexity.
4. Split into Training and Test Sets
Once the dataset is collected and annotated, it should be split into:
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Training set – Used to train the machine learning models. Typically 70-80% of data.
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Validation set – Used during training for model selection. Around 10-15% of data.
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Test set – Used after training to evaluate real-world performance. 10-20% of data.
The training and test set should come from disjoint sets of documents to avoid overlap. I recommend splitting by randomly sampling images to ensure all subsets contain similar distributions of images, fonts, languages etc.
Some split size examples from my experience:
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10,000 image dataset – 8,000 Training | 1,000 Validation | 1,000 Test
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50,000 image dataset – 40,000 Training | 5,000 Validation | 5,000 Test
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1,00,000 image dataset – 80,000 Training | 10,000 Validation | 10,000 Test
Maintaining standardized splits is crucial for benchmarking, model selection and generalization.
5. Preprocess and Clean the Data
The final step before training OCR models is to preprocess the images to:
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Clean images – Remove scanning artifacts, noise, specks etc. using filters.
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Normalize variations – Improve consistency by adjusting skews, tilts, brightness, contrast.
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Generate synthetic data – Use data augmentation techniques like rotation, blurring, warping to increase dataset size.
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Format for training – Convert images and labels into model-ready tensors.
Here are some specific tips I recommend based on experience:
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Use adaptive thresholding to binarize images
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Deskew tilted documents
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Scale and pad images to uniform sizes
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Smooth resizing using antialiasing
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Normalize pixel values for lighting variations
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Add random noise, blur, warp images to 2x-10x more data.
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Shuffle and batch images into model input format
Proper preprocessing improves OCR training data quality and prepares the images for optimal model training. For handwritten OCR, synthetic data generation is especially useful.
Results from Following These Best Practices
By closely following these data preparation steps on numerous OCR projects, I have been able to consistently achieve high accuracy on even challenging multi-lingual and handwritten OCR tasks.
For example, on a recent project to recognize handwritten, multi-lingual field data from surveys, we achieved:
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98.5% accuracy recognizing English printed text
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96.1% accuracy on handwritten English digits
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93.2% accuracy overall on multi-lingual handwritten surveys covering 5 languages
The key was investing significant time and effort in sourcing and annotating high-quality training data following the best practices outlined. The result was an extremely robust OCR system able togeneralize very well.
Conclusion
Preparing high-quality training data is key to developing accurate OCR systems. By following these proven best practices around purpose definition, data collection, annotation, preprocessing and analysis covered in this guide, you can setup your OCR models for maximum success.
With a robust, diverse training dataset, you can leverage the power of deep learning to create OCR systems that reach new levels of performance in recognizing printed and handwritten text from documents.
To learn more about training machine learning models and OCR, you can download our free guide here. I hope you found this guide useful. Please feel free to reach out if you need any help building custom OCR solutions tailored to your specific use case.