An OCR API helps you transcribe text from images and multi-page PDF documents and receiving the extracted results in a JSON format. OCR scans images of documents, invoices, receipts, recognizes and extracts text from them, and transcribes it into a format for interpretation by the machines.
How does an OCR API work?
OCR scans and analyzes 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 -
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, all with 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 provides you with the entire medical history of a patient on a searchable, digital store. 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 transcribe text appropriately -
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 develop a layer of 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.
1. Tilted Text in Scanned Documents
OCR tools are incompetent in incorporating object detection in their operation. 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
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 model or use de-noising image tools.
Use Cases of OCR APIs
Here are some use cases of OCR APIs that you cannot overlook, and thus, you must not drop the idea of employing OCR for your business requirements -
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%.
2. Legal documents
OCR APIs can transcribe forms of documents such as affidavits, judgments, filings, and more which can ease up information browsing.
OCR can analyze cheques, read and update passbooks, ensure KYC compliance, analyze loan applications and other services.
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.
OCR APIs can automate reading bills, invoices and receipts, and extract products, prices, company names for the retail and logistics sector.
Top OCR APIs in 2021
Here are five OCR APIs in the market that are the preferred choices for a substantial amount of users -
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.
4. 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 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.
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 and requires a minimal setup. The platform offers a 70% reduction in processing costs and a whopping 50% efficiency boost.
Some distinctive features offered by Docsumo when converting and processing scanned documents include -
- Auto Classification
- Validation Rules
- Data Capture
- API Integration
- Fraud Detection
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.
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