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A Beginner’s Guide to OCR APIs

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.

How does an OCR API work?

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.

Applications of OCR API

Here are some real-world applications of OCR API in several sectors that can help streamline document scanning and processing jobs -

1. Banking

‍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.

2. Legal

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.

3. Healthcare

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).

Limitations of OCR APIs

Here are some aspects where OCR APIs fall short and fail to perform text extraction accurately -

Product shortcomings

1. Incompetency with working on custom data

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.

2. Substantial requirement for post-processing

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.

3. Satisfactory results only in specific constraints

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.

Technological limitations

1. Tilted text in scanned documents

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.

2. Natural scenes

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.

3. Handwriting and varied fonts

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.

4. Multi-language text recognition

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.

5. Blurry scanned documents

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.

OCR APIs - Different use-cases

Here are some use cases of OCR APIs of how you can extract data from unstructured documents and convert into structured documents/editable format:-

1. Logistics

Logistics OCR API

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 reduce 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 fraud. Suppliers and businesses streamline communications by sending electronic invoices over email and getting faster order confirmations.

2. Legal documents

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 services help in ensuring the safety, integrity, and privacy of legal documents as well.

3. Banking‍

Banking OCR API

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.

4. Healthcare

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.

5. Accounts Payable

Accounts Payable API

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 a better customer experience by ensuring data accuracy and integrity.

Best OCR APIs in 2021

Here is a list of some of the best OCR service providers in the market that can help you in automating data entry and digitizing your business operations: -

1. Docsumo

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 in 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 -

  • Document auto classification
  • Validation rules
  • Data capture
  • API integration
  • Fraud detection


  • Works with unstructured documents and processes invoices, bank statements, receipts, IRS tax forms, and many more.
  • Uses intelligent OCR and AI to perform key value pair extraction.
  • Is able to recognize and process handwritten notes, scanned images, and photo on photo.
  • Prevents document fraud and forgeries, and is great at verifying data authenticity through validation checks.
  • Users can set custom data parsing rules and optimize data annotations.
  • Can convert documents into various file formats such as JSON and XML
  • Uses a pay-as-you-use model and is not restricted to specific subscription fees.


  • Lack of integration with popular accounting software platforms at the present moment.


  • Ask for pricing.

2. Google Document AI

‍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 documents to facilitate compliance, and provides insights to help satisfy customer expectations. It also improves CSAT, lifetime value, advocacy, and spend.


  • Uses intelligent OCR to automate data extraction and can read scanned images.
  • Can process large volumes of unstructured text without human intervention.
  • Has multilingual support and uses AI models to convert processed documents in over 200 languages.


  • Customization of existing APIs and pre-processing rules can take a lot of time and effort.


  • Pricing starts at $65 for 1000 pages.

3. Amazon Textract

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.‍


  • Uses AI-based services to extract data from unstructured texts, PDFs, invoices, and various business documents.
  • Can extract nested tables, handwritten notes, and scanned documents.
  • Features key value extraction and uses Natural Language Processing (NLP) to categorize lines, form data, and page elements.


  • All the code is open-source and there are hard limits set such as limited page and height widths for PDFs, file size limits, support for only specific file formats, and many more restrictions.


  • The company also offers 1000 free pages per month for the first three months. Textract OCR bundles cost anything between $0.60 to $1.50 per 1000 pages. This pricing is subject to consumption and geographical location.

4. Abby Flexicapture

‍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.‍


  • Transforms unstructured documents into business-ready data.
  • Features award-winning document classification technology.
  • Is highly scalable and offers a robust customizable architecture.


  • Is slow and can cause delays in document processing at times due to the nature of its build.


  • Ask for pricing.

5. Rossum AI

‍‍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 enable 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.


  • Parses invoices, shipping labels, documents, receipts, and many other document types.
  • Is up to 6X faster than manual processing and uses AI algorithms to bulk process large volumes of documents automatically.
  • It is scalable and is completely Cloud-based.


  • Does not allow users to derive insights, do real-time monitoring of data, or track analytics metrics.


  • Ask for pricing.

Final Words

OCR APIs offer several benefits in diverse sectors by automating the 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.

Suggested Case Study
Automating Portfolio Management for Westland Real Estate Group
The portfolio includes 14,000 units across all divisions across Los Angeles County, Orange County, and Inland Empire.
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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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