Optical Character Recognition (OCR) has transformed data processing, enabling the conversion of text images from scanned documents into machine-readable text. This technology, eliminating manual data entry, seamlessly digitizes diverse paper-based documents, whether handwritten or printed.
In the finance domain, precision and speed are crucial, making OCR a game-changer, especially in Order to Cash (O2C) processes. AI-powered OCR significantly enhances efficiency, particularly in check processing and cash application.
This article delves into the intricacies of OCR and its profound impact on streamlining O2C processes, emphasizing its role in digitizing physical documents for a smoother cash flow.
Optical character recognition (OCR) is the electronic conversion of text images from sources like scanned documents into machine-readable text. It eliminates the manual transfer of information, enabling easy digitization of diverse paper-based documents, handwritten or printed, into digital formats.
This process facilitates easy storage and accessibility, enabling efficient search and retrieval of information from previously inaccessible sources. It enhances data management by converting images into editable and searchable text, making it a valuable tool for digitizing vast quantities of documents across languages and formats.
The steps involved in OCR processing are as follows.
The first step in OCR processing involves capturing the document as an image. The OCR software will then utilize this scanned image and classify the image into light and dark areas. The light areas correspond to the image background and the dark areas correspond to the text present in the image. The OCR software will check the dark areas to extract information.
To ensure accurate data capture, the OCR software will further clean the captured image and check for any aberrations. Some of the image cleaning techniques utilized are deskewing the scanned image to fix alignment issues, despeckling to remove any digital spots or smoothen the edges of the images, and cleaning additional texts and boxes present in the image.
Once the background and text characters have been separated, the OCR will now be processing the characters to extract valuable data. This is done in two ways: i) pattern recognition ii) feature recognition.
Pattern recognition: This is utilized to recognize alphabetic letters and numbers present in the captured image or document. Here the OCR software takes each character and matches it against the characters/glyphs already stored in the OCR software. Pattern recognition works only when the stored characters are of the same font and scale as the input characters. So, pattern recognition works well only when the document is typed and cannot be used for handwritten documents.
Feature recognition: Here instead of looking at characters, the OCR looks at character attributes such as the lines, strokes, angles and lines intersections of the characters. Basis this it then matches this against the stored glyphs.
This is the final step in OCR processing and here the OCR software further checks for accuracy to avoid any errors. During this step, the extracted text is then converted into a digital file. These files generally are present in pdf or spreadsheet format.
The effectiveness of OCR in enhancing efficiency in back-office processes has been paramount. OCR not only enhances efficiency but allows people to focus on higher-value tasks by enabling the automation of tasks that are highly manual in nature. Within finance, order to cash (O2C) is a major function where the application of OCR is seen to be immensely beneficial.
Two core areas under O2C where OCR is seeing increasing adoption are check processing and cash application. An important factor why the effectiveness of OCR is significant in O2C processes is that most documents handled during the O2C processes such as check, and invoices are highly structured in nature, which enhances the efficiency and accuracy of OCR in digitizing these documents.
While digital payments and overall digitalization have seen an increasing uptake, a lot of processes under O2C still need to deal with a lot of physical documents such as checks, invoices and remittance advice. The processing of such documents requires a considerable workforce to manually enter data into the system. This is not only cumbersome but is time-consuming and there are high chances of errors during documentation.
Delays and errors during manual processes such as check processing result in delays in revenue recognition, which impacts the overall cash flow. Additionally, any error in capturing the payment and remittance information affects the next step in the cash application process that of invoice matching. Discrepancies at this stage along with the team being unable to find the correct data in the system increase the days in accounts receivable (AR), which further impacts cash flows.
OCR has the potential to effectively manage these challenges by digitizing these physical documents and making them readily accessible. With OCR these digital files can now be accessed by stakeholders who can now easily search the documents and find all key information related to invoices and payment in real-time that speeds up the overall workflow starting from data aggregation to invoice matching.
While digital payments and e-check usage have increased, there are still many instances where organizations need to process checks and remittance information. This information can be shared by banks in the form of scanned checks. In such scenarios, the O2C teams need to spend considerable time manually capturing the data from these scanned check images increasing the chances of error in data capture as well as delay in payment realization.
OCR check processing addresses these challenges by auto-extracting the required information from traditional checks and images with ease and accuracy in a fraction of the time as compared to manual processing. Not only does OCR decrease check processing time considerably but it ensures that all stakeholders can access data that streamlines the cash application process which ultimately results in healthier cash flows.
Some of the key features of OCR check processing are:
OCR check processing enables organizations to empower and enable their AR teams to focus on higher-value tasks by automating check processing. Some of the major benefits of OCR check processing are:
Automated data entry: OCR automates the extraction of key information from physical documents and images into an electronic file. This conversion of analog text to digital text makes the data machine-readable and enhances check processing efficiency.
Accelerated check processing: With OCR-enabled automated data capture the checks are digitized at a considerably less time as compared to manual processing. The accelerated check processing expedites the payment recognition process starting with cash application.
Increased data accuracy: OCR reduces the human errors that occur during manual data capture processes. The increased accuracy improves the overall workflow and enhances efficiency.
Increased team productivity: The replacement of the manual data entry process by OCR enhances team productivity as they can now focus on higher-value tasks. Further, as the data is captured accurately it helps the team to seamlessly carry out the related tasks without errors.
Improvement in data accessibility: OCR stores data in a centralized location that can be readily accessed by the team in real time. This improves the visibility of data among the stakeholders.
Improvement in data searchability: Due to digitization all information is readily available and all stakeholders can easily access the data and search the document. This helps the team in processes such as invoice matching.
Improved cash flows: Due to the benefits offered by OCR check processing such as accelerated processing, reduction in error and easy accessibility of data, the effectiveness of subsequent steps in 02C such as cash application improves which reduces the days in AR with faster payment recognition. All these factors cumulatively contribute to healthier cash flows.
While Optical Character Recognition (OCR) significantly boosts the speed at which crucial payment information becomes available in a central repository, its impact is maximized when paired with a robust cash application solution. HighRadius’s AI-based cash application software achieves a 95% straight-through cash posting rate, facilitates same-day cash application, and delivers a 100% reduction in lockbox key-in fees.
Equipped with an AI-based, multi-OCR engine, the software ensures a seamless and highly accurate data capture process during check processing, eliminating unnecessary noise. The solution efficiently scans through various document formats almost instantly, allowing informed decisions on payment applications. Seamlessly integrating with ERP systems, credit agencies, payment partners, banking institutions, and other third-party applications, HighRadius’s cash application streamlines data workflows and eliminates the need for manual data entry.
An OCR (Optical Character Recognition) model is a type of technology or system designed to recognize and extract text content from images, scanned documents, or other visual inputs. The goal of an OCR model is to convert non-editable text, such as printed or handwritten text on paper, into machine-readable and editable text. at the word level.
The steps involved in OCR processing are image acquisition, preprocessing, character recognition and postprocessing.
The OCR performance metrics are character accuracy, word accuracy, page accuracy, character error rate, word error rate, page error rate, confusion matrix, precision, recall, and F1 score.
Word error rate measures the difference between recognized words and the ground truth, considering insertions, deletions, and substitutions.
Character error rate quantifies the difference between the recognized text and the ground truth by considering insertions, deletions, and substitutions.
There isn’t a single “best” OCR benchmark that universally suits all OCR applications. The choice of an OCR benchmark depends on the specific requirements and characteristics of the application for which OCR is being employed. Some widely recognized benchmarks and datasets used in OCR evaluation are IAM handwriting database, MNIST, synthetic datasets, ICDAR (International Conference on Document Analysis and Recognition) datasets, COCO-Text.
Organizations should choose a benchmark that closely aligns with the specific OCR task they are working on (e.g., printed text recognition, handwritten text recognition, scene text recognition). The benchmark data should be representative of the data that the OCR system will encounter in real-world scenarios. Further, organizations should understand the evaluation metrics used in the benchmark and whether they align with the organization’s performance criteria.
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