Executive Summary
This insight explores the significance of Optical Character Recognition (OCR) in enhancing efficiency and accuracy in data handling. OCR converts documents into searchable, editable data, streamlining workflows and reducing manual effort. Key applications include document digitization, data entry automation, and document validation. V2A successfully utilized OCR with Microsoft Azure to automate financial statement processing, cutting down weeks of work to less than a day. Despite challenges like poor image quality and handwritten text variability, OCR’s benefits—such as improved accuracy and scalability—make it indispensable for modern organizations. V2A offers expertise in deploying OCR solutions to optimize business processes.
Introduction
Integrating digital solutions is a proven way for organizations to respond to changing markets and innovate in their fields. Though the impact of these solutions may vary across organizations, the fact that digitalization has become a necessity rather than a luxury is evidenced by global spending trends on digital transformation technologies and services, which increased at a compounded annual growth rate (CAGR) of 14% from 2017 to 2022, with projections reaching $3.9 trillion in 2027¹.
Among the myriads of digital solutions available, Optical Character Recognition (OCR) stands out as a crucial technology that can significantly enhance efficiency and accuracy in data handling. OCR is a system encompassing a series of processes to identify text in images and replicate it in a machine-readable format (ex. ASCII). By converting different types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data, OCR allows organizations to streamline workflows, reduce manual data entry, and improve overall productivity.
OCR Applications
- Document digitization: One of the most common applications of Optical Character Recognition (OCR) is converting physical archives into digital formats to make the text within those documents searchable and editable. This type of application is shared across a wide range of industries, given the need for established companies to modernize their systems. A common use case of this application is the digitization of patient records, such as X-ray readings, treatment plans, and tests, which enables them to be accessed digitally by authorized healthcare providers⁵. At V2A, we have leveraged Microsoft’s Azure Document Intelligence, a cloud-based service powered by advanced machine learning models, to enhance data management within our financial sector practice. Each quarter, in order to publish V2A’s Quarterly Banking Report, the firm’s financial sector consultants extract more than two hundred financial statements from local financial and lending institutions published by the local financial sector regulator. These statements are provided in a PDF format and had previously required two-to-three weeks to process manually. Now thanks to Azure Document Intelligence’s OCR capabilities, we can automatically read these financial statements, extract the relevant data, and tabulate it according to user-defined specifications (see Figure 1), a process that takes less than a day to complete.
- Data entry automation: This application uses Optical Character Recognition (OCR) to extract specific data points from documents and automatically enter them into target systems or databases. In banking, for example, OCR enables bank clients to deposit a check into their account with the snap of a picture⁵. At V2A, we have also applied OCR in this capacity in a client setting, using a Robotic Process Automation (RPA) application that processed employee timesheets in a government agency. The OCR algorithm was specifically used to navigate menus, perform actions (ex. write to input fields), and extract results in client-side software. This automation was able to complete a week’s worth of manual work in three hours, which helped our client comply with an aggressive timeline.
- Document verification/validation: This approach is like data entry automation in that it involves specific data extraction from images; however, instead of inputting that data into a database for further analysis, the purpose is to validate documents as part of a more extensive process, such as flagging users whose documents fail to meet specific criteria. At V2A, we have developed an OCR document validation solution as part of our support with process automation in a public sector client. Specifically, a state’s central procurement office was tasked with manually validating purchase authorization letters submitted by other agencies (in scanned PDF format) during the procurement process. Our OCR system automated this validation by confirming that the submitted documents were the required ones. Additionally, it extracted the purchase amount from the requisition document and verified that it did not exceed the budget approved by the Office of Management and Budget, as indicated in the corresponding budget document. The system also ensured that the budget account referenced in the requisition document matched the one in the budget approval. In summary, our solution automated the validation of procurement documents, including data extraction and cross-referencing, while providing alerts for corrective actions, ultimately accelerating the procurement process (see Figure 2).
- As a tool to feed Retrieval Augmented Generation (RAG) databases: Retrieval Augmented Generation is a technique that customizes a Large Language Model’s responses by connecting it to a proprietary data lake. In cases where these data lakes contain information that must be extracted from image documents or PDFs, an OCR workflow can be used to periodically feed it data.
How does OCR work?
Optical Character Recognition (OCR) can be broken down into the following steps:
- Preprocessing – This step involves cleaning the image and identifying essential attributes to make it easier for the algorithm to read it in later steps. Cleaning an image involves tasks such as aligning text, removing spots and distortions, and turning the document into a grayscale image to increase the contrast between the background and the foreground, which helps discern the elements that will be extracted later² ³. Relevant attributes may include script recognition for OCR that extracts text in different languages or dialects.
- Text Extraction – Text extraction contains three components:
- Segmentation: The program isolates text by drawing bounding boxes around it.
- Feature Extraction: The program breaks down the characters within the drawn boxes into sets of attributes (AKA features), such as lines (defined by their orientation), curves, or closed loops³ ⁴. This step usually involves a computer vision algorithm to detect the features³ ⁴.
- Classification: The program uses a machine learning classification algorithm trained on character features to identify the characters in the image based on the extracted features.
- Postprocessing – The transformation of the text extraction results into a computer file. Usually, the postprocessing step includes quality assurance measures to validate OCR outputs and could even require human supervision for complex documents² ³.
Advantages and Challenges
Given its applications in digitization and automation, using OCR has the following advantages over using manual solutions:
- Improved accuracy: A well-designed OCR solution can consistently provide a more accurate reading of the documents being digitized than a manual solution, which is prone to human error and inconsistent performance.
- Efficient Workflows: Digital solutions can perform tasks quickly so that users can get their desired outputs in real time.
- Scalability: OCR systems can be scaled to handle increased volumes of document inputs at a lower cost than staff augmentation.
- Integration with other workflows: Document digitization and validation tasks are often among the first steps for converting data into insights. Additional tasks, which might require analysis of the data in those documents are likely to follow and OCR systems can feed the newly digitized documentation into those workloads with little need for supervision.
Despite the clear advantages of implementing OCR, certain challenges must be overcome to achieve them. These include:
- Poor image quality, which makes it harder for models to classify the characters in the document as it makes them more ambiguous and harder to detect. To account for poor image quality, the preprocessing stage of the OCR system must be adjusted to the specific patterns of flaws of an organization’s documentation³.
- Handwritten text has a lot more variation than printed text, which also contributes to the ambiguity of the characters that OCR systems must transcribe. To account for this, the deep learning models that classify the characters in the image must be trained on handwritten data³.
- Multi-language documents may contain characters from different alphabets or with different grammatical rules that can change the characters (ex. the accented letters in Spanish). This requires the models in the system to be trained in the characters of different languages.
Conclusion
By leveraging sophisticated tools such as machine learning, OCR demonstrates its power and versatility in converting various documents into machine-readable data, driving efficiency and innovation across sectors. As this technology evolves, its potential benefits far outweigh the challenges, making it essential to collaborate with experts who deeply understand AI and OCR.
At V2A, we specialize in implementing AI-driven solutions tailored to your organization’s needs. Let us partner with you to harness the power of OCR and other AI technologies to optimize your processes, reduce costs, and stay ahead in a competitive market. Contact us today to start transforming your business.
Disclaimer
Accuracy and Currency of Information: Information throughout this “Insight” is obtained from sources which we believe are reliable, but we do not warrant or guarantee the timeliness or accuracy of this information. While the information is true and correct at the date of publication, changes in circumstances after the time of publication may impact the accuracy of the information. Information may change without notice and V2A is not in any way liable for the accuracy of any information printed and stored, or in any way interpreted and used by a user.
Sources
- https://www.statista.com/statistics/870924/worldwide-digital-transformation-market-size/
- What is OCR? – Optical Character Recognition Explained – AWS (amazon.com)
- Optical Character Recognition Technology for Business Owners (mobidev.biz)
- OCR with Deep Learning: How Do You Do It? | Label Your Data
- Optical Character Recognition (OCR) – The 2024 Guide – viso.ai
