AI-powered OCR API
The Cost-saving
Resume Parsing Software
Valitract extracts every data field from resumes and CVs — name, contact, education, work history, skills, and certifications — and delivers structured output from any format in under 3 seconds, with 98% accuracy and a 40% cost saving compared to other resume parser software. Whether you call it resume parsing or CV parsing, the result is the same: clean, structured candidate data ready for your systems.
Upload a Resume.
See Parsing in Action
Drop any resume or CV file and watch Valitract parses every candidate detail — name, email, phone, education, experience, skills, and certifications — into a structured output in seconds. Our OCR resume scanning engine reads PDF, DOCX, and image files in any language, including OCR CV processing for international candidates. Download as Excel, copy as JSON, or push straight to your ATS or HR system.
Drop, Upload and Paste your file here to Extract Text from Image
Supported file types: PNG | JPG | JPEG
Your privacy is protected! Your files are secure and never stored.
Data security is our top priority
Valitract is built with security and privacy as foundational principles. Candidate data is processed securely and never retained after extraction. We follow GDPR-aligned data handling practices, with SOC 2 Type II and ISO 27001 certifications currently in progress.

One Resume Parsing Softwarefor Every Hiring Workflow
Recruitment & Staffing Agencies
Corporate HR & Talent Acquisition
Job Boards & Career Platforms
ATS & HR Software Vendors
Background Check & KYC Providers
RPO & Outsourced Hiring
Why choose Valitract
to parse resumes
Automated Resume Data Extraction
Valitract works as an intelligent resume extractor that captures every key candidate field — full name, contact details, education history, work experience, skills, certifications, and languages — and outputs them to your preferred structure or format. Automated resume data extraction means no manual input required.
3 Seconds Per Resume
Your recruiting team spends an average of 15 minutes per resume on manual data entry alone. Valitract’s resume OCR software cuts that to under 3 seconds — even for multi-page CVs, scanned documents, and image-based files.
Reduced Operational Costs
At $0.05–$0.10 per resume, Valitract helps you save 40% of operating cost compared to other resume parsing tools and cv parsing software. No per-page pricing, no hidden fees, no setup charges. Unlike legacy OCR resume solutions that bill by the page, Valitract charges per document — regardless of length.
Seamless ATS & HR Integration
JSON output plugs directly into BambooHR, Workday, Greenhouse, Lever, SAP SuccessFactors, or any custom applicant tracking system with minimal engineering effort. Valitract functions as an ATS resume parser that feeds clean candidate data into your hiring pipeline. Zapier and Make automations available out of the box.
Scalable Solution
Effortlessly scale to handle growing candidate volumes, processing over 100,000 resumes per hour with 99.9% uptime. Built for recruitment agencies and enterprise hiring teams who need bulk processing without slowdowns.
Candidate-Grade Data Privacy
Resumes contain sensitive personal data. Valitract processes documents securely and never retains candidate information after extraction. Full audit logs, encryption in transit and at rest, and GDPR-aligned practices protect every applicant’s privacy.
Parse Any Resume
in 4 Easy Steps
Supported formats include: jpg, png, gif, jpeg, tiff, webp, and more.
The easiest
Resume Parsing API
Designed for real engineering teams — not demo-ware. Valitract’s resume parsing API turns any document into structured candidate JSON with a single call. Built as an AI resume parser from the ground up, it handles any layout, language, or format. Full SDK support. Webhook-ready. Handles high-volume bursts without rate-limit surprises.
import java.io.*;
import java.net.*;
import org.json.*;
public class ValitractOCR {
private static final String API_KEY = "your_api_key_here";
private static final String API_URL = "https://api.valitract.com/api/v1/extract-generic";
public static void main(String[] args) {
try {
File file = new File("document.pdf");
String result = processOCR(file);
System.out.println("Extracted Data: " + result);
} catch (Exception e) {
e.printStackTrace();
}
}
public static String processOCR(File file) throws Exception {
HttpURLConnection conn = (HttpURLConnection) new URL(API_URL).openConnection();
conn.setRequestMethod("POST");
conn.setRequestProperty("Authorization", "Bearer " + API_KEY);
conn.setDoOutput(true);
// Upload file and get response
return "JSON Response";
}
}package main
import (
"bytes"
"encoding/json"
"fmt"
"io"
"mime/multipart"
"net/http"
"os"
)
const (
apiKey = "your_api_key_here"
apiURL = "https://api.valitract.com/api/v1/extract-generic"
)
type OCRResponse struct {
FileName string `json:"file_name"`
ExtractedData map[string]interface{} `json:"extracted_data"`
}
func processOCR(filePath string) (*OCRResponse, error) {
file, err := os.Open(filePath)
if err != nil {
return nil, err
}
defer file.Close()
body := &bytes.Buffer{}
writer := multipart.NewWriter(body)
part, _ := writer.CreateFormFile("file", filePath)
io.Copy(part, file)
writer.Close()
req, _ := http.NewRequest("POST", apiURL, body)
req.Header.Set("Authorization", "Bearer "+apiKey)
req.Header.Set("Content-Type", writer.FormDataContentType())
client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
return nil, err
}
defer resp.Body.Close()
var result OCRResponse
json.NewDecoder(resp.Body).Decode(&result)
return &result, nil
}
func main() {
result, err := processOCR("document.pdf")
if err != nil {
fmt.Println("Error:", err)
return
}
fmt.Printf("Extracted Data: %+v\n", result)
}const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const API_KEY = 'your_api_key_here';
const API_URL = 'https://api.valitract.com/api/v1/extract-generic';
async function processOCR(filePath) {
try {
const form = new FormData();
form.append('file', fs.createReadStream(filePath));
const response = await axios.post(API_URL, form, {
headers: {
'Authorization': `Bearer ${API_KEY}`,
...form.getHeaders()
}
});
console.log('Extracted Data:', response.data);
return response.data;
} catch (error) {
console.error('Error processing OCR:', error.message);
throw error;
}
}
// Usage
processOCR('document.pdf')
.then(data => console.log('Success:', data))
.catch(err => console.error('Failed:', err));<?php
define('API_KEY', 'your_api_key_here');
define('API_URL', 'https://api.valitract.com/api/v1/extract-generic');
function processOCR($filePath) {
if (!file_exists($filePath)) {
throw new Exception("File not found: $filePath");
}
$ch = curl_init();
$cfile = new CURLFile($filePath, mime_content_type($filePath), basename($filePath));
$data = [
'file' => $cfile
];
curl_setopt_array($ch, [
CURLOPT_URL => API_URL,
CURLOPT_POST => true,
CURLOPT_POSTFIELDS => $data,
CURLOPT_RETURNTRANSFER => true,
CURLOPT_HTTPHEADER => [
'Authorization: Bearer ' . API_KEY
]
]);
$response = curl_exec($ch);
$httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);
curl_close($ch);
if ($httpCode === 200) {
return json_decode($response, true);
} else {
throw new Exception("API Error: $response");
}
}
// Usage
try {
$result = processOCR('document.pdf');
echo "Extracted Data:\n";
print_r($result);
} catch (Exception $e) {
echo "Error: " . $e->getMessage();
}
?>import requests
import json
API_KEY = 'your_api_key_here'
API_URL = 'https://api.valitract.com/api/v1/extract-generic'
def process_ocr(file_path):
"""
Process OCR on a document using Valitract API
Args:
file_path (str): Path to the document file
Returns:
dict: Extracted data from the document
"""
headers = {
'Authorization': f'Bearer {API_KEY}'
}
with open(file_path, 'rb') as file:
files = {
'file': (file_path, file, 'application/pdf')
}
try:
response = requests.post(API_URL, headers=headers, files=files)
response.raise_for_status()
result = response.json()
return result
except requests.exceptions.RequestException as e:
print(f'Error processing OCR: {e}')
raise
# Usage
if __name__ == '__main__':
try:
data = process_ocr('document.pdf')
print('Extracted Data:')
print(json.dumps(data, indent=2))
except Exception as e:
print(f'Failed to process document: {e}')require 'net/http'
require 'uri'
require 'json'
API_KEY = 'your_api_key_here'
API_URL = 'https://api.valitract.com/api/v1/extract-generic'
def process_ocr(file_path)
uri = URI.parse(API_URL)
File.open(file_path, 'rb') do |file|
request = Net::HTTP::Post.new(uri)
request['Authorization'] = "Bearer #{API_KEY}"
boundary = "-----------RubyMultipartBoundary"
request['Content-Type'] = "multipart/form-data; boundary=#{boundary}"
post_body = []
post_body << "--#{boundary}\r\n"
post_body << "Content-Disposition: form-data; name=\"file\"; filename=\"#{File.basename(file_path)}\"\r\n"
post_body << "Content-Type: application/pdf\r\n\r\n"
post_body << file.read
post_body << "\r\n--#{boundary}--\r\n"
request.body = post_body.join
response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) do |http|
http.request(request)
end
if response.code == '200'
JSON.parse(response.body)
else
raise "API Error: #{response.body}"
end
end
end
# Usage
begin
result = process_ocr('document.pdf')
puts "Extracted Data:"
puts JSON.pretty_generate(result)
rescue StandardError => e
puts "Error: #{e.message}"
endusing System;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Threading.Tasks;
using Newtonsoft.Json;
namespace ValitractOCR
{
public class OCRClient
{
private const string API_KEY = "your_api_key_here";
private const string API_URL = "https://api.valitract.com/api/v1/extract-generic";
public static async Task<string> ProcessOCR(string filePath)
{
using (var client = new HttpClient())
{
client.DefaultRequestHeaders.Authorization =
new AuthenticationHeaderValue("Bearer", API_KEY);
using (var form = new MultipartFormDataContent())
{
var fileContent = new ByteArrayContent(
File.ReadAllBytes(filePath)
);
fileContent.Headers.ContentType =
MediaTypeHeaderValue.Parse("application/pdf");
form.Add(fileContent, "file", Path.GetFileName(filePath));
var response = await client.PostAsync(API_URL, form);
response.EnsureSuccessStatusCode();
var result = await response.Content.ReadAsStringAsync();
return result;
}
}
}
public static async Task Main(string[] args)
{
try
{
var result = await ProcessOCR("document.pdf");
Console.WriteLine("Extracted Data:");
var formatted = JsonConvert.DeserializeObject(result);
Console.WriteLine(
JsonConvert.SerializeObject(formatted, Formatting.Indented)
);
}
catch (Exception ex)
{
Console.WriteLine($"Error: {ex.Message}");
}
}
}
}Try Resume Parsing Software
for Free
Start for free with complimentary credits and dedicated developer support to help you get set up quickly.
Frequently asked question
Find quick solutions to common queries and get the most out of your learning experience
Resume parsing software reads a resume or CV document and extracts candidate data — name, contact details, education, work experience, skills, and certifications — into a structured format like JSON, Excel, or CSV. Valitract layers AI on top of traditional OCR to understand any resume layout, regardless of format, language, or design style, without needing predefined templates — and at a fraction of what most tools charge.
Most resume parsing tools on the market rely on templates or predefined rules, meaning they break when they encounter an unfamiliar layout. They charge per page, deliver results through dated interfaces, and require extensive training before they work reliably. Valitract is template-free, priced at $0.05–$0.10 per document, achieves 98% field accuracy, and outputs clean, ready-to-use structured data in one click. Whether you need to extract candidate details from a handful of applications or automate resume processing at scale via API, the workflow is the same: upload, extract, export.
Valitract achieves 98% data extraction accuracy across all supported resume formats and layouts. The AI is trained on millions of resume variations — from minimalist one-page CVs to multi-page creative portfolios — and includes a validation layer that automatically flags any low-confidence fields so your team can review them before they reach your ATS.
Valitract handles comprehensive resume information extraction, covering all standard candidate fields: full name, email address, phone number, physical address, date of birth, education history (institution, degree, field of study, graduation date), work experience (company name, job title, employment dates, responsibilities), technical and soft skills, certifications and licenses, language proficiencies, and links (LinkedIn, portfolio, GitHub). The output is returned as structured JSON with consistent field names for easy integration.
Yes. Valitract returns clean JSON output, making it immediately compatible with any system that accepts structured data — including BambooHR, Workday, Greenhouse, Lever, SAP SuccessFactors, iCIMS, and custom-built ATS platforms. Pre-built connectors are available for the most popular systems, and Zapier and Make integrations allow no-code automation without developer involvement.
Valitract handles resumes in any layout — single-column, multi-column, creative, tabular, and academic formats. It supports 30+ languages and character sets, including Latin, Cyrillic, Chinese, Japanese, Korean, Arabic, and Hindi. Handwritten notes and signatures on scanned resumes are also recognized.
Input: PDF, DOCX, DOC, JPG, JPEG, PNG, TIFF, WEBP, GIF, and BMP. Output: Excel (.xlsx), CSV, or JSON — your choice. The Excel output is cleanly formatted with proper column headers — one row per candidate, one column per field — ready to open in Excel, Google Sheets, or any spreadsheet tool immediately. The JSON output is schema-consistent and integrates directly with any downstream system, ATS, or database.
Yes. Valitract’s OCR engine processes scanned documents, photographed resumes, and image-based files with the same accuracy as native digital PDFs. Whether a candidate faxed their CV, a recruiter photographed a paper resume at a career fair, or an applicant uploaded a JPG screenshot, Valitract extracts the data reliably.
A free tier includes 50 documents per month at no cost. Paid plans start at $0.05–$0.10 per document, compared to $1–$5 per resume for competing resume parsing tools or $10+ per resume for manual data entry. Enterprise volume pricing is negotiated directly based on monthly throughput.
