Organizations dealing with high volumes of documents often need to convert them to digital formats for processing. Optical character recognition (OCR) and artificial intelligence (AI) offer two approaches to tackle these document digitization needs.
This article provides an in-depth comparison between OCR and AI technology. It covers how both work, their unique strengths and limitations, real-world use cases and guidelines for selecting the right approach based on specific requirements.
OCR Technology
Explanation of OCR Technology
OCR refers to the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It captures text from document images and detects fonts, formatting and layout to recreate documents digitally.
Benefits of Using OCR Technology
The biggest benefit offered by OCR technology is the ability to quickly and accurately convert high volumes of scanned documents into searchable and editable formats like Word, Excel and PDF. This eliminates tedious manual retyping.
OCR improves accessibility for visually impaired people when integrated with assistive tools like text-to-speech. It also serves for machine translation applications.
Limitations of OCR Technology
The accuracy of OCR technology depends heavily on the quality of scanned documents. Non-standard fonts, poor image quality, creases and ink stains can drastically impact text recognition accuracy.
OCR performs limited contextual analysis of processed documents. It lacks capabilities such as categorizing document types or extracting relationships between entities.
Examples of Industries Using OCR Technology
OCR technology sees widespread use across areas like:
- Healthcare: Digitizing patient records and prescriptions
- Banking: Processing checks and application forms
- Insurance: Claims processing
- Libraries: Digitizing archives
- Legal: Contract analysis
AI Technology
Explanation of AI Technology
AI technology refers to training artificial neural networks on vast datasets to emulate human intelligence for various applications like computer vision, speech recognition and document processing.
For document analysis, deep learning algorithms are used to develop capabilities like comprehending layout, data extraction and document classification.
Benefits of Using AI Technology
AI technology delivers higher accuracy compared to rules-based software across edge cases involving elements like poor quality scans, non-standard layouts and unique fonts. It can also handle handwritten input.
Additionally, AI can categorize documents, extract fields and tables to generate structured data, a feat not possible through OCR alone. Some AI chat pdf programs now even come with OCR technology integrated within them. This eliminates error-prone manual data entry.
As AI models process more data, they continually enhance accuracy and learn new patterns without explicit re-programming. Custom models can also be trained to handle domain-specific documents.
Limitations of AI Technology
The performance of AI models heavily relies on the quality and size of training data. For many real-world applications, procuring large volumes of representative data remains challenging.
AI technology is also expensive as it requires high-performance hardware and continual optimization by data scientists to be effective. Interpretability continues to be a key challenge.
Examples of Industries Using AI Technology
AI is gaining rapid adoption across sectors like:
- Financial: Processing invoices, bank statements and payment advice documents
- Government: Structuring forms and extracting information for easy retrieval
- Healthcare: Assisting with clinical decision support by analyzing patient health records
- Insurance: Automating claims processing using computer vision and NLP
Comparison of OCR and AI
We compare OCR and AI technology across four key aspects:
Speed and Accuracy
OCR offers faster turnaround times when document formats are standardized. Accuracy drops significantly for unstructured or handwritten documents.
AI analysis is slower but can handle documents with greater variability more accurately through continual learning.
Complexity and Adaptability
OCR software is simpler to implement but adapting to new document types requires explicit re-programming. AI systems are inherently more complex but can be re-trained on new data without code changes.
Cost-Effectiveness
OCR is cheaper to set up but requires extensive manual work for data entry and quality checks. AI automation provides vastly higher ROI but needs upfront investments into solutions and infrastructure.
Integration with Existing Systems
OCR tools have standardized integration capabilities with common formats like PDF and Office files. AI integration requires bespoke work tailored to current IT landscapes involving trade offs.
Conclusion
OCR converts documents to digital equivalents rapidly but lacks intelligence. AI provides in-depth understanding of documents and extracts actionable information. It comes at a higher price tag but the return on investment is unmatched.
With the rising complexity of documents, AI promises to be the path forward for organizations looking to truly automate document-based processes instead of simply digitizing paper. The approach warrants consideration especially when dealing with large volumes of varied documents.
That said, OCR continues to be relevant for several use cases given its simplicity, speed and lower costs. Weighing requirements around accuracy, cost and integration with legacy systems is important when deciding between the two approaches.
