Contact Extraction to Spreadsheet

@NIKITALILA

Hello There, We can assist with extracting and organizing contact details from PDFs, webpages, and scanned documents into a clean, mail-merge-ready spreadsheet. Our preferred approach is OCR where appropriate, followed by manual verification to ensure accuracy. We can separate emails, phone numbers, and mailing addresses into dedicated columns, remove duplicates, standardize formatting, and cross-check questionable entries against the source documents. The final file can be delivered in Excel, CSV, or Google Sheets, based on your preference. Turnaround will depend on the volume and complexity of the source files, and we can confirm the timeline once the scope is shared. We typically work on an hourly basis, from 9:30 AM to 6:00 PM IST, Monday to Saturday. Please note that making and receiving phone calls is not included in our service scope. Hit me up and we can talk in detail Regards, Nikita and her team at Affiliazion (D)

@onlinejob247

Dear client, I can do this job of Contact Extraction to Spreadsheet accurately as per your requirements and available to start immediately. Thanks!

@sobhad20

Ready to start now* - Delivery within 24 hours Hi There, I can do this project right now with perfectly. Kindly ping me to start your work now. Regard’s Sobha

@tmoi06

Hello Sir, I can extract every valid contact from your PDFs, webpages, and scanned documents into a clean, mail-merge-ready spreadsheet with careful deduplication and accuracy checks. ✅ Why Me? ✔ Extensive experience with PDF data extraction, OCR, web research, data entry, and contact-list building ✔ Can combine Python/OCR automation with manual verification for scanned or difficult-to-read documents ✔ Extract emails, phone numbers, and complete mailing addresses into clearly separated, standardized columns ✔ Strong focus on deduplication, formatting consistency, and identifying incomplete or questionable records ✔ Comfortable delivering Excel, CSV, or Google Sheets according to your preferred workflow ✔ 500+ projects completed | 5.0-star rating For a typical batch, I can turn around the completed dataset within 1–3 business days, depending on the number and complexity of files. I can also provide a small sample first so you can confirm the extraction format and accuracy before full processing. Best regards, Ayan

@TeraByte26

Hi Mary, I will extract every email, phone number and mailing address from your PDFs, webpages and scanned images into a duplicate‑free Excel/CSV with separate columns ready for mail‑merge. I’ll deliver the cleaned file within three business days for $500. I can send a quick sample today—could you confirm the total number of files? Waiting for your response in chat! Best Regards.

@ganeshrasekar

I can do it

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in Web Scraping, OCR As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@hirujiyu

╰┈➤ DATA EXTRACTION & WEB SCRAPING | 8+ YEARS EXPERIENCE Hi there, I can help extract complete and accurate business contact data from PDFs, webpages and scanned documents and organize everything into a clean, mail-merge-ready spreadsheet. My approach: ✔️ Python-based extraction + web scraping ✔️ OCR for scanned/image-based documents ✔️ Extract emails, phone numbers & mailing addresses ✔️ Normalize formatting into separate columns ✔️ Remove duplicate contacts ✔️ Validate extracted data and manually review exceptions ✔️ Deliver Excel, CSV or Google Sheet as required For larger document sets, I can combine automated extraction with validation checks and manual sampling rather than relying only on OCR or scraping. ⭕ How many source documents/pages are there approximately? ⭕ Do you need only contact details, or company/name/title fields as well? ➥ Timeline: 2–5 days depending on document volume ➥ Rate: $15/hr ➰ Share a few sample files and I can confirm the extraction approach and workflow before processing the complete collection. Thank you.

@Hrolgar

Start with a 10-file pilot: a mix of PDFs, webpages and scanned images, run through the pipeline before anything touches the full set. Which files are digital text and which need OCR decides where the errors will be, and I'd rather find that out on a small batch than after the whole spreadsheet is built. The method is Python. PDF text layers and HTML get parsed directly, and scans go through OCR as a fallback. Emails, phone numbers and addresses come out with regex, then validation: syntax and domain checks on emails, phone formatting to one consistent style, and addresses split into street, city, postcode and country columns so mail merge doesn't choke. Duplicates get merged on the normalized value. Every row carries a source-file column, so any contact can be traced back and spot-checked against its origin. For accuracy, I'd hand-check a random sample of rows against the originals, with the scanned images checked more heavily since OCR is where digits get misread. Anything low-confidence goes on a separate review sheet rather than being silently dropped or silently included. I haven't seen your files yet, so I can't promise an error rate up front. The pilot gives me a real number. M1: Pilot on 10 files, extraction rules tuned, sample spreadsheet, $211, 1d. M2: Full run across all PDFs, webpages and scans, dedup and validation, $256, 2d. M3: Sample-based accuracy check, review sheet, final mail-merge-ready spreadsheet, $173, 2d. Roughly how many files are in the set, and are the scans clean photos or low-res faxes?

@XRProConnect

Hi, Aashiq here from Cape Town, South Africa. This project instantly caught my eye, so I had to reach out. I see you’re looking for accurate contact extraction from various documents, including PDFs and scanned images. I understand how critical accuracy is for your outreach campaigns. I have extensive experience in data extraction, helping businesses streamline their contact lists through efficient methods like Python scripts, OCR, and manual checks. I’m confident I can deliver a clean spreadsheet with every valid contact organized perfectly. Feel free to ask for samples of my previous work. Based on what you mentioned, here is how we would approach the project: - Use Python and OCR for extraction from PDFs and images. - Implement checks to ensure no duplicates or formatting issues. - Deliver the final output in your preferred format. You can expect clear communication throughout, ensuring a seamless, user-focused solution optimized for your needs. Best Regards, Aashiq

@rimiloca

Hi, I can turn your supplied business documents into a clean Excel/CSV contact table. I would extract text directly from PDFs and webpages, use OCR only for scans, then verify uncertain characters against the original. Each record would keep its source reference so you can trace it back. I will separate email, phone and address fields, preserve country codes, normalise formatting and remove duplicates without merging different people or companies by mistake. I will flag uncertain or missing data rather than guess it. My $300 / 5-day estimate covers up to 100 source documents and 2,000 business contact records; I will confirm the exact batch after seeing representative files. Could you share the document count and a redacted sample?

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ Hi Client, thank you for checking my proposal and sharing the detailed project brief. I can extract and compile your contacts from various business documents using Python, OCR tools, and web-scraping utilities with a clean, organized design. I will deliver: • Accurate extraction of emails, phone numbers, and addresses • Organized data in separate columns without duplicates • Thorough verification through manual checks and automated scripts • Final output in your preferred format (Excel, CSV, or Google Sheets) • Timely updates throughout the process to ensure transparency You will also receive documentation on the extraction process for future reference. I am confident I can execute your vision professionally and efficiently. Looking forward to discussing timeline and next steps. Best regards, Chirag.

@carlosd178

Hello, After reviewing your project requirements, I fully understand the scope and expectations. I have experience with data extraction, web research, OCR processing, and organizing business information into clean, structured spreadsheets. For this project, I would combine automated extraction methods (Python scripts, OCR tools, and parsing techniques) with manual verification to ensure contacts from PDFs, webpages, and scanned images are captured accurately. My workflow would include extracting emails, phone numbers, and addresses, normalizing the data format, removing duplicates, checking for missing or invalid entries, and delivering a clean Excel/CSV/Google Sheet ready for outreach campaigns. I have a couple of quick questions. • Approximately how many source documents need to be processed? • Are there any specific spreadsheet columns or CRM import formats you need the final file to follow? I would be glad to review the source files and start building an accurate contact database. Best regards, Carlos.

@Georgeoustar

Hi there! Your project’s focus on extracting accurate, well-organized contacts from a variety of business documents is clear, and I understand how essential precision and clean formatting are for your outreach campaigns. I have extensive experience in data extraction using a combination of Python scripting, advanced OCR (for scanned images), and web scraping tools—tailored to each file type for maximum efficiency and accuracy. To ensure the data is both complete and reliable, I pair automated extraction with careful manual verification and cross-referencing to eliminate duplicates and standardize formatting for seamless mail-merge. The end result will be a meticulously structured spreadsheet, delivered in your preferred format. To clarify your needs: are there specific data fields or formats you require for each contact? Additionally, how many documents should I anticipate processing? Understanding this will help me align my approach exactly to your expectations. Looking forward to your reply! Best Regards, George

@arslanshahid386

Hi, I understand you need contacts extracted from various business documents, including PDFs, webpages, and scanned images, into a spreadsheet for outreach campaigns. Ensuring accuracy is crucial for your project as these contacts are essential for your outreach efforts. The primary challenge lies in the diversity of source formats, especially scanned images, which may require OCR (Optical Character Recognition) to accurately extract data. My approach involves first assessing the quality of the documents and prioritizing a robust extraction process to maintain data integrity and accuracy. Ensuring that the extracted data is organized into separate columns for emails, phone numbers, and mailing addresses is key to delivering a clean and usable spreadsheet. I have extensive experience with web scraping and data extraction, having successfully completed similar projects that involved complex data sources and formats. Q1 – What is the estimated volume of documents to be processed? Q2 – Are there any specific fields or data points beyond contact details that need extraction? Q3 – Do you have any preferred tools or formats for the final spreadsheet delivery? Looking forward to hearing from you.

@muhammadi476

The main challenge here isn’t simply extracting contacts—it’s making sure the final list is complete, deduplicated, and clean enough to use directly for outreach without fixing it afterward. I’d use a combination of Python-based extraction for PDFs/webpages, OCR for scanned documents, and manual checks for anything ambiguous. I’ll structure emails, phone numbers, and full addresses into separate columns, normalize formatting, remove duplicates, and flag or exclude invalid/incomplete entries rather than guessing. For verification, I’ll cross-check extracted data against the original source and run validation checks on emails, phone formats, duplicates, and column consistency before delivery. I can provide the final dataset in Excel or CSV, depending on your preference. For a typical batch, I can turn it around within 24–48 hours after receiving the files, with the exact timeline confirmed once I see the volume and document types. Feel free to send a sample of the documents first—I can extract a small sample so you can review the accuracy and formatting before we proceed with th

@longngoc2307

Perfect Match "ACCURATE CONTACT DATA EXTRACTION" — you need contacts from PDFs, webpages, and scanned documents consolidated into a clean, deduplicated spreadsheet ready for outreach. I’d use Python for structured extraction and normalization, OCR for scanned pages, and targeted parsing for web sources. Email, phone, and address fields would be standardized into separate columns, then deduplicated using normalized contact values rather than simple text matching. For verification, I’d flag low-confidence OCR results and run validation checks for malformed emails, inconsistent phone formats, incomplete addresses, and duplicate records before delivery. What is the approximate number of source files and total pages, including scanned documents? Looking forward to work with you.

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@Synet

Hi, I would write a custom Python script using BeautifulSoup for the webpages and PyPDF2 or Tesseract OCR for the scanned images and PDF files, pulling every email, phone number, and mailing address into clean columns without any formatting quirks that break a mail merge. To verify accuracy, I will run a script to cross check records, remove duplicates, and manually spot check a random sample of the extracted contacts before delivery. I can export the final set as an Excel workbook or a live Google Sheet based on your preference. One smart addition I can make is adding a column that tags each contact by its source document so you know where it came from. Are the scanned images mostly clean text, or do some have handwritten notes and low resolution? Best, Salma Noreen