Top 10 OCR Resume Parser API
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Top 10 OCR Resume Parser API

This article is brought to you by the Eden AI team. We allow you to test and use in  production a large number of AI engines from different providers directly through our API and platform. You are a solution provider and want to integrate Eden AI, contact us at : contact@edenai.co

In this article, we are going to see how we can easily integrate an OCR Resume Parser engine in your project and how to choose and access the right engine according to your needs.

Definition:

Optical Character Recognition, also called OCR, is a technology that recognizes text within a digital image. The basic process of OCR involves examining the text of a document and translating the characters into code that can be used for data processing. OCR engines are made up of a combination of hardware and software that is used to convert physical documents into machine-readable text. Hardware to copy or read text while software typically handles the advanced processing.

Resume Parsing is conversion of a free-form resume document into a structured set of information suitable for storage, reporting, and manipulation by software. Resume parsing helps recruiters to efficiently manage electronic resume documents sent electronically.

History:

OCR traces its roots back to telegraphy. On the eve of the First World War, physicist Emanuel Goldberg invented a machine that could read characters and convert them into telegraph code. In the 1920s, he went a step further and created the first electronic document retrieval system.

Early versions of OCR had to be trained with images of each character and were limited to recognising one font at a time. In the 1970s, inventor Ray Kurzweil commercialised “omni-font OCR”, which could process text printed in almost any font.

OCR Technology became popular in the early 1990s while attempting to digitize historic newspapers. In the early 2000s, OCR became available online as a cloud-based service, accessible via desktop and mobile applications.

Today, there’s a host of OCR service providers offering technology (often accessible via APIs) capable of recognising most characters and fonts to a high level of accuracy.

Top 10 Resume OCR API:

Sovren

Sovren is a privately held software-components-only firm established in 1996. Sovren develops and licenses Resume/CV Parsing and Semantic Matching / Artificial Intelligence Matching (AIM) components. The Sovren Resume Parser is used within human resource software, and on recruitment web sites and portals, to simplify and accelerate the application process, to extract and classify thousands of attributes about the candidate, and to provide a foundation for the semantic searching of candidate data. The Parser identifies hundreds of different kinds of information within a resume or CV, and clearly tags each data point (for example: first name, last name, street address, city, educational degrees, employers, skill, etc.). The output may be used in HR-XML or JSON format.

Textkernel

Textkernel’s highly accurate resume parser, supports recruiting organizations around the world to effectively and efficiently process large volumes of candidate documents. Textkernel offers parsing in 24 languages and can process documents in any format, such as pdf, docx, doc, html, odt, txt, etc. It can also process scanned documents by applying OCR (Optical Character Recognition). Texternel uses state-of-the-art AI to extract information regardless of the document structure, language or writing style. The AI is built using documents and speakers in the native language, without using quality-degrading machine translations.

Affinda - Available on Eden AI

Affinda provides artificial intelligence technologies for the automation of high volume document workflows. They help companies process unstructured data, removing the need for high-cost and error-prone manual processes. Affinda's approach is deliberately designed to overcome the typical issues associated with AI investment. Underpinning our approach is our proprietary technology in combination with the latest advancements in natural language processing, image recognition, and transfer learning. Affinda's technology is industry agnostic, and applies to any document type, including emails, contracts, invoices, forms, financial statements and more.

Daxtra 

Daxtra Technologies is a world-leading specialist in high-accuracy, multilingual resume and job parsing, semantic search, matching and recruitment automation. Daxtra offers a competitive edge in finding the best available talent, while keeping the cost-per-hire to a minimum. Their products seamlessly integrate with our clients’ existing systems and processes helping to find relevant information quickly and intuitively, across multiple internal and external databases.

Daxtra is an innovative technology that automatizes every step of the way, with Capture, Search, Parser amongst others. It automates different parts of the recruiting process – and seamlessly integrates with your existing recruitment or resume management software.

HireAbility

HireAbility uses a software called ALEX to process all your data. HireAbility’s parsing software supports any resume, CV and job posting layouts including social media profiles. ALEX can parse resumes in over 40 languages and dialects including multiple languages and multiple locations in one resume or CV. ALEX (Automated Linguistic EXpert), HireAbility’s CV / Resume parser and Job Order parser employs several AI strategies including natural language processing techniques and pattern recognition in order to parse relevant information from resumes written in a free-text format.

SuperParser

SuperParser is an enterprise grade resume parsing API deployed in a number of Applicant Tracking Systems (ATS), internal recruitment teams, HR technology platforms and job boards – ranging from tiny start-ups all the way through to large enterprises. SuperParser API can parse resume of all format : Microsoft Word (all versions including DOCX), PDF, Text (.txt), Rich Text (RTF), OpenOffice 2. SuperParser API will process documents in a matter of seconds.

RChilli

RChilli is known for providing parsing, matching, and enrichment to every recruitment management system. We offer resume parser/cv parser powered by AI and NLP, enabling users to extract the relevant information from resumes of different formats and easy analysis.

hire EZ

hireEZ (previously Hiretual) is an AI-powered outbound recruitment platform to help you break free from outdated recruiting systems and help jobs find people. With hireEZ, you can execute a strategically scalable approach to build your workforce of the future. hireEZ's Resume Parsing API autofills application forms with information from uploaded resumes to substantially simplify the application process for candidates. You can also use that structured data to apply analytics and extract insights about your talent pool.

HR Flow

HrFlow.ai is an AI powered API that brings intelligence to your HR Data and gives you the freedom as a developer or an HR architect to build an infinite number of scenarios that meet your business logic.It comes with a complete and a fully integrated suite of HR data processing products -- such as Parsing API, Revealing API, Embedding API, Scoring API and Reasoning API. You just have to connect your existing tools and start automating workflows.

Use cases:

All HR Professionals deal with CVs and Resumes that they need to analyze manually. Resume parser APIs are useful for all the companies that need to process and analyze big amounts of resumes. Those companies can parse resumes by using directly resumer Parsing APIs or they can do it through a HR software that is using resume parsing APIs.

Open source VS API

When you need an OCR resume parser engine, you have 2 options:

  • First option: multiple open source OCR engines exist, they are free to use. Some of them can be performant but it can be complex to set up and use.Using an open source AI library requires data science expertise and you will need to add some computer vision and NLP to get a good OCR resume engine. Moreover, you will need to set up a server internally to run open source engines.
  • Second option: you can use ready-to-use engines which are provided by OCR resumer parsing specialists. This option looks very easy because you don't need any AI abilities and you don't need to train any model. You just have to process your data into the API.

The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.

This method is the best in terms of performance and optimization but it presents many inconveniences:

  • you may not know every performant providers on the market
  • you need to subscribe and contract with all providers
  • you need to master each providers API documentation
  • you need to check their pricing
  • You need to process data in each engine to realize the benchmark

Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including OCR for resume. The platform allows you to benchmark and combine results from different engines thanks to a standardized response format for all the providers.

Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call receipt parser engines with a provider as a simple parameter.

Test and API:

Here is the code in Python (documentation) that allows to test Eden AI for Resume parser:

import json
import requests
from pprint import pprint
 
headers = {'Authorization': 'Bearer + API key'}
 
with open("invoice_english.png", 'rb') as f:
  response = requests.post("https://api.edenai.run/v2/ocr/resume_parser" , 
                              headers=headers,
                              data={'providers': "['affinda']", "language":"en-US"},
                              files={"files": f})
    
result = json.loads(response.text)
pprint(result)

Platform:

Eden AI Platform: OCR Resume parser

Conclusion:

There are numerous OCR resume parser engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate resume parser technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.

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