An Optical Character Recognition(OCR) API helps you transcribe text from image files and PDF documents and receive the extracted data in a JSON/CSV/Excel or other file formats. OCR scans images of documents, invoices, receipts, recognizes and extracts text from them, and transcribes it into a format for interpretation by the machines. OCR APIs are built on OCR technology but what differentiates them is that they are trained to extract data from specific documents, and that’s why they are more accurate.
What is OCR API and how does it work - let's find out in the blog.
OCR APIs scan and analyze the framework of document images and breaks down the page into blocks of tables or text lines. These lines are then subdivided into words and eventually into characters.
Once the OCR tool singles out individual characters, it analyzes them against a set of pattern images. The program then formulates a series of hypotheses to figure out the nature of the symbol.
As per these devised hypotheses, the program analyzes several variants of segregating lines into words and words into characters. Once the program appropriately concludes the identity of the scanned symbol, it displays the interpreted text.
Here are some real-world applications of OCR API in several sectors that can help streamline document scanning and processing jobs -
The Banking industry, alongside other finance sector industries such as insurance and securities, relies significantly on OCR. OCR scans and transcribes handwritten data from checks, bank statements, different forms, and profit/loss statements, all without any human involvement.
The automation of interpreting information from a check has reduced turnaround time for check clearance, which is an economic gain for everyone, from payer to bank to payee.
Tall heaps of affidavits, filings, judgements, wills, statements, and other printed legal documents can get digitized, stored, and made searchable by implementing simple OCR readers.
For an industry that largely relies on judicial precedent, swift access to legal documents from millions of past cases is necessary, a leap that is achievable because of OCR.
OCR can help arrange the entire medical history of a patient in a searchable database derived from unstructured medical reports. This implies that things such as past illnesses and treatments, hospital records, diagnostic tests, insurance reimbursements, and more are accessible in a unified place.
Since the entire record of a hospital can get stored digitally, this can significantly aid epidemiology (prevalence of diseases) as-well-as logistics (maintaining suitable stores of equipment, drugs and other consumables).
Here are some aspects where OCR APIs fall short and fail to perform text extraction accurately -
OCR requires unique algorithms to handle different types of data. OCRs are untrainable if the text displayed is in another format than horizontal text. For instance, current OCR APIs cannot read vertical characters, making the detection task tedious and inconvenient.
If you wish to use the extracted text from a scanned invoice, you have to design the parsing rules for the OCR software that allows you to extract dates, sum amount, product details, and other information. This step implies that you require an in-house developers team to use existing OCR APIs and build software for the intelligent structuring of data.
Current OCR methods yield satisfactory results on scanned documents that contain digital text. However, handwritten documents that contain multiple languages, low-resolution images, and other non-ideal scenarios can cause your OCR model to display errored-results and render low accuracy.
OCR tools find it difficult to detect objects. Because of this, an OCR model cannot recognize the characters and words that are tilted. Under such cases, the text appears tilted and cannot be considered acceptable for the realm of automation.
OCR models fall short when extracting text from images shot in a variety of settings. An OCR tool finds the first character and traverses in a horizontal direction, searching for subsequent symbols. However, if the image is blurry, the font is unrecognizable, or the text is tilted, OCR fails to yield satisfactory results.
The OCR annotation process identifies each character as an individual bounding box by spotting the gaps between words that remain absent in handwritten text or cursive fonts. Without these gaps, the OCR model acknowledges that all the characters are a single pattern and do not fit into any character descriptions.
Most OCR models work satisfactorily for English but remain incompetent for other languages. This incompetency occurs because there is not enough training data or syntactical rules for various languages. You cannot rely on OCR when analyzing documents that contain multiple languages, such as forms, to deal with government processes.
OCR models often generate wrong results when dealing with noisy images. Your OCR model can get baffled between ‘8’ and ‘B’ or ‘A’ and ‘4’. The only way to tackle noisy images is to implement Deep Learning in your OCR solution or use de-noising image processing tools.
Here are some use cases of OCR APIs of how you can extract data from unstructured documents and convert into structured documents/editable format:-
OCR can capture data from bills of lading, shipping labels, delivery notes, invoices, and purchasing orders in real-time. It lets you extract key-value pairs, validate tax rates and amounts, and reduces back-office costs by up to 50%. OCR APIs in logistics use smart data extraction to process forms and many other documents. The logistics industry deals with huge volumes of information and OCR APIs streamline communications between vendors, suppliers, and buyers by providing accurate information and converting unstructured documents into structured types.
By ensuring data accuracy, these APIs can eliminate re-corrections involved with entering incorrect amounts, process CMR waybills, and detect document frauds. Suppliers and businesses streamline communications by sending electronic invoices over email and getting faster order confirmations.
OCR APIs can transcribe forms of documents such as affidavits, judgments, filings, and more which can ease up information browsing. Legal firms benefit from OCR technology that helps attorneys save case files in electronic formats, thus reducing the need for paper-based document storage. Law firms save data in a lot of online directories and OCR APIs are extremely helpful in this regard. Another advantage is multilingual conversions and processing legal documents in different languages based on client requirements. Several OCR APIs help attorneys scan, edit, and store legal documents safely online. OCR service helps in ensuring the safety, integrity, and privacy of legal documents as well.
OCR can process data from cheques, passbooks, bank statements, KYC documents and other documents. Banks use OCR APIs to process financial statements, authenticate transactions, and verify account standings. OCR ensures that the turnaround times for banking institutions are fast by helping them verify account numbers, transaction details, identity and tax details from different financial documents. Loan origination and administrative tasks can be automated by combining OCR APIs with AI and machine learning for processing customer applications.
OCR APIs can transcribe the medical records of patients, history of illnesses, medication, and more, helping cut down the time spent doing such tasks manually. AI-based OCR technology can be used for scanning prescription slips, lab notebooks, clinical trial data, and converting them into digital formats for safe patient record keeping. Healthcare companies can scan numerous fields from different medical documents using these APIs and streamline patient onboarding processes in hospitals. Another exclusive feature is that these APIs can educate patients on their rights, safety concerns, and medical treatment options by scraping, extracting, sorting, and organizing appropriate medical data. OCR APIs also ensure legal compliance when it comes to maintaining medical documentation systems and workflows in hospitals and healthcare institutions.
OCR engines can automate reading bills, invoices and receipts, and extract products, prices, company names for the retail and logistics sector. OCR can recognize different invoice layouts and extract essential fields with 95% accuracy. Data capture solutions and OCR APIs for invoices can perform data validation on scanned images and convert them into excel/json/csv for analysis. For businesses that want to keep stock of inventory and issue pre-orders, invoice scanning can help optimize budgets and perform cash flow analysis based on financial statements. In short, OCR data extraction in invoices can assist companies in deriving insights from data and lay the groundwork for providing better customer experiences by ensuring data accuracy and integrity.
Here is the list of some of the best OCR service providers in the market that help you in automating data entry and digitizing your business operations: -
Document AI software integrated with Intelligent OCR technology facilitates the smart conversion of unstructured documents, including pay stubs, invoices, and bank statements, to actionable information.
This OCR API works with all types of documents, different formats, and requires a minimal setup. You can upload pdf files or scanned images in jpg/png/tiff image formats and extract text with 99%+ accuracy.
Some distinctive features offered by Docsumo when converting and processing scanned documents include -
Google Document AI platform is a unified console for document processing meant for automatically classifying, extracting, and enriching data within your documents to provide insights.
The DocAI platform validates all the documents to facilitate compliance, and provides insights to help satisfy customer expectations. It also improves CSAT, lifetime value, advocacy, and spend.
Amazon Textract is a wholly managed machine learning tool that automatically extracts handwriting, printed text, and other information from scanned documents.
Textract employs machine learning to read and process any document type instantly, and helps extract handwriting, forms, printed text, tables and other information precisely with no manual effort or custom code.
ABBYY Flexicapture is an Intelligent Document Processing platform that can handle any document type and every job size, be it from ad hoc single documents to large batch jobs that require tough SLAs.
Flexicapture feeds content-driven business applications that include RPA and BPM, which lets organizations emphasize customer service, compliance, cost reduction, as-well-as competitive advantage.
Rossum AI seamlessly transcribes complex structured scanned documents, which can facilitate companies to extract data from financial credentials with human-level accuracy.
Rossum AI understands complex structured documents, which enables companies to extract data from financial documents efficiently and with human-level accuracy. Rossum's unique deep neural networks illustrate the way humans refer to documents.
OCR APIs offer several benefits in diverse sectors by automating jobs of transcribing documents. This convenience lets workers emphasize the core tasks of a company. However, this process also comes with its drawbacks, some of which have gotten tackled via Deep Learning.
Besides the shortcomings, OCR is still considered reliable and beneficial for companies that deal with digital heaps of digital documents and require speedy transcribed results.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.