# Financial Document OCR Specialist Required

@asifdwan

Hi, I can handle the extraction of all ~800 financial statement PDFs using a hybrid OCR/document-AI + Python workflow, including Arabic/English scans, rotated/skewed pages, table structures, comparative years, auditor details, and financial periods. I’ll validate figures through balance/total checks, cross-period consistency, account mapping, confidence scoring, and manual review of uncertain values—never guessing—and will structure everything directly into your Excel template with traceability. I’m happy to complete the required pilot first and, if the results meet your 98%+ accuracy requirement, proceed with the full batch.

@ahmadayaz

With over 5 years of experience in data engineering, OCR technologies, and financial domains, I propose using Tesseract OCR and machine learning for data extraction from scanned PDFs. Python scripts will handle data transformation, with manual intervention for accuracy above 98%. Validation involves cross-referencing with financial standards and reconciliation techniques. Pilot testing will demonstrate accuracy and efficiency, ensuring alignment with expectations. Pricing reflects complexity and expertise for high-accuracy extraction. Let's discuss further to ensure a successful partnership for your financial statement OCR project.

@Prutheus

800 phone-scanned financial statements into a panel dataset. Phone scans are the hard part: skew, shadows and uneven lighting break naive OCR, and financial tables need the column structure preserved, not just the text. How I would run it: - Preprocessing per page: deskew, contrast normalisation, shadow removal, so the OCR gets a clean image - Table-aware extraction rather than plain text OCR, so line items keep their column alignment - Mapping into your Excel template with validation: totals cross-checked against components, years and company names normalised - Anything failing validation flagged for manual review rather than silently written - Pilot on your 5 samples first, delivered exactly as the full run would be, so you can judge accuracy before committing I run Python OCR and document pipelines in production for industrial clients. One question: are all documents the same statement type and layout family, or do formats vary across companies? Martin

@eshA0010

Hi, I can handle this as a structured financial-data extraction project, combining OCR/document processing with manual validation to achieve the 98%+ accuracy you require. I will use Python-based data processing and OCR/document extraction tools for the initial capture, followed by Excel-based validation and manual review for low-confidence fields, Arabic/English content, rotated scans, and unclear numerical values. For accuracy, I will cross-check current vs. comparative-year figures, validate totals and financial relationships where applicable, verify line-item mapping against your template, and flag uncertain values rather than guessing. I will also maintain clear traceability back to the source PDF. I have experience with Excel, Python, data extraction, financial data, document processing, and large structured datasets. I’m happy to complete the pilot sample first so you can verify my accuracy before awarding the full 800-PDF project. I can provide a firm total price after reviewing the sample PDFs and your extraction template.

@MuhammadUzair16

Hi, I am a software engineer with over 16 years of experience, including Python data engineering, OCR pipelines, document processing, and structured financial-data extraction. For these Arabic and English phone-scanned statements, I will combine image preprocessing (rotation, deskewing, denoising, and contrast correction) with suitable document-AI/OCR engines and Python-based line-item mapping. Accuracy will be protected through confidence scoring, current-versus-comparative-year checks, accounting equation and subtotal validation, cross-page consistency checks, and manual review of every uncertain value. Nothing ambiguous will be guessed; it will be clearly flagged, with source-page traceability maintained in the Excel output. I can begin with the required pilot and refine the mapping rules against your template before processing the full set. I have proposed $160 because 800 PDFs and a verified 98%+ target require substantial validation beyond basic OCR, though I am flexible after reviewing the sample, page count, and template. Are the statements based on broadly consistent formats, and approximately how many pages does each PDF contain? Please contact me to discuss details.

@muneeb6767

I can handle this with Python, OCR/document-AI, PDF preprocessing, Pandas and Excel, using deskew/rotation cleanup, Arabic+English OCR, account mapping, cross-year consistency checks and validation against source totals; uncertain values will be flagged rather than guessed. I’d start with the required pilot, refine the extraction/validation pipeline from your feedback, then process the 800 PDFs with traceable outputs and QA focused on 98%+ accuracy; I can provide a fixed quote after reviewing the sample/template.

@qualityxenter

Affordable, Early Delivery. ★★★★★★★★★★★★★★I hold a Masters degree which gives me the requisite background to handle writing from various subjects. I am a highly committed person towards my work. You can rely on QualityXenter for quality and consistency in writing. We never violate copyright rules. I have vast amount of experience in this industry since I am working from 2015 as a professional writer. I provide many modifications till to get your satisfactions. I have access to enough journals to use in your research project. I always produce quality work at VERY LOW RATES so, don't worry if you have a low budget for your work, I will be very happy to make a new client like you. I am producing quality work for my clients including ARTICLE WRITING, REPORT WRITING, ESSAY WRITING, RESEARCH PAPERS, BUSINESS PLAN, TECHNICAL WRITING, MATLAB, THESIS, ACCOUNTING & FINANCE work ETC. Go through my profile link https://www.freelancer.com/u/qualityxenter

@jawadaziz78

Assalam o alaikum, Before I claim 98% accuracy, I'd want to hit it on your pilot sample in order to validate my accuracy which is most important for Financial statements I'll use Claude Fable 5.1 for the extraction and mapping, then review every uncertain or inconsistent value by hand. For validation I'll check the extracted figures against the original statements myself comparative-year values, totals, dates, company details, auditor information. I've done a similar job for a client in Spain, pulling structured data out of scanned financial records. For the 800 PDFs, I'd rather price after the sample is done.

@shakilan4

Hi, I can accurately extract and structure the financial data from your 800+ scanned PDFs using Python, OCR/document-AI tools, image preprocessing, and automated validation. I’ll carefully map line items, current/comparative values, company and financial-year details, auditor information, report dates, and financial periods into your Excel template. To achieve 98%+ accuracy, I’ll use confidence scoring, accounting cross-checks, totals validation, and manual review for low-confidence or inconsistent results. Uncertain figures will always be flagged rather than guessed. Arabic and English documents are also supported. I’m happy to start with your pilot sample so you can verify the accuracy before the full project. I can provide a reliable, structured, and traceable dataset suitable for academic research. Best regards, Shakila Naz

@CauaAlves

Hi, I can build an automated OCR and data extraction pipeline in Python to accurately extract the tabular financial data from your 800 phone-scanned PDF statements. Pipeline architecture: • Pre-Processing Engine: image correction for phone scans (deskewing, adaptive thresholding, contrast enhancement, shadow removal) to maximize OCR character recognition. • Hybrid Extraction (Tesseract / Vision + Layout Parser): - Segmenting balance sheet, income statement, and notes into structured tabular coordinates. - Extracting line-item descriptions, period dates, currency labels, and numerical values. • Validation & Reconciliation: - Automatic balance validation (Assets = Liabilities + Equity check). - Cross-checking column totals to immediately flag extraction anomalies. • Structured Export: consolidated Excel workbook and clean CSV master table with company ID, statement type, fiscal year, line item, and reported amount, structured specifically for academic empirical analysis. I work extensively with financial statements, Python data engineering (pandas, pdfplumber, OpenCV), and quantitative data prep. To test: could you share 2 or 3 sample PDFs from different companies? I will run them through the pre-processor and show you the extracted table format.

@seospider

Hello, For 98%+ accuracy, I’d use OCR/document-AI with Python validation, financial cross-checks, confidence scoring, and manual review—uncertain values will always be flagged, never guessed. I’ve worked with financial statements, transaction classification, structured Excel datasets, Python/data processing, and reconciliation workflows where traceability matters. I’d validate totals, comparative years, account mappings, dates, and company/auditor fields before final export. Can you share 3–5 sample PDFs and the Excel template for the pilot? Are Arabic and English mixed within the same statements? Best Regards Hasan

@mahnoorb78

With my extensive experience in data extraction and analysis, as evidenced by the 500+ projects I've successfully completed over the past five years, I am uniquely positioned to handle your Financial Statement Data Extraction project. I'm proficient in all tools associated with OCR and document-AI processes and will utilize them alongside a comprehensive validation and verification strategy to ensure an accuracy level surpassing your desired target of 98%. Furthermore, my familiarity with multi-lingual texts - including Arabic - ensures a fluid run-through of any language combinations that may arise from the phone-scanned PDFs. This added expertise is invaluable when dealing with low-quality scans or when documents are rotated, skewed, or messy. On top of these core competencies, my strong background in Python and Data Engineering makes processing, mapping and extracting complicated datasets a task that comes naturally. To start off with confidence in our shared commitment to this project's success, I offer to perform a pilot test using a small sample for your verification before we move forward with the full project. My proposed price for approximately 800 PDFs tailoring to all your requirements is $[your proposed price here]. You can trust me to bring not only efficiency and skill but also a dedication to excellence needed for this important study.

@ziedb4

Hello, Answering your five questions directly. Tools: Python pipeline. Pre-processing with OpenCV for deskew, rotation correction and contrast normalisation, since phone scans fail mostly at this stage, not at OCR. Then a document-AI layer for table structure, with Tesseract as a cross-check. I read Arabic and English natively, which matters here: I can verify Arabic line items myself rather than trusting an OCR score. How I reach 98%+: not by trusting one engine. Every number is checked against the statement's own internal arithmetic. Assets must equal liabilities plus equity. Subtotals must equal the sum of their line items. Current-year and comparative columns must reconcile with the prior year's filing where both exist. A number that fails its own checksum is wrong regardless of how confident the OCR was. Validation: two independent extractions per document, with any disagreement flagged. Plus the arithmetic checks above. Anything unresolved goes into the output as flagged, never as a guess, exactly as you asked. Experience: Python data pipelines and structured extraction are my regular work. [Add one line naming a specific extraction or data-engineering project you delivered.] Price: I will quote after the pilot. Ten documents chosen by you, covering your worst scans, delivered in your template with the flagged fields visible. That tells us both what the real accuracy is before either of us commits to 800.

@aamirpitafi

The risk is not just missed values—it is current and comparative figures being mapped to the wrong period or account, leaving an unreliable research dataset. I’d start with a pilot using OCR and image cleanup for rotated, skewed, low-quality Arabic and English scans. I would map the required figures and metadata into your Excel template, validate totals, period alignment, and line-item context, and flag unclear values for review rather than guess. I have 20+ years of software engineering experience and use Python for custom data-processing workflows. What will count as 98% accuracy for pilot approval: individual fields, numeric values, or fully correct statements?

@ass19800

Hello, I have previously completed a U.S. financial data project, where I processed 2025 income and benefits data using Excel. For this project, I will use Python, OpenCV, ABBYY/Tesseract OCR, and Excel automation, with accounting cross-checks and manual review to achieve 98%+ accuracy. I recommend starting with a small pilot before processing all 800 PDFs. My estimated price is $50 for the full project. Thank you, Alex

@rofeald

Hi, I can build a reliable OCR and validation workflow for the 800 scanned financial statements, including messy/rotated documents and Arabic-English content. I’d use Python with document OCR/AI tools to extract financial line items, current and comparative values, company details, financial periods, auditor information, and report dates into your Excel template. Rather than trusting OCR output directly, I’ll apply table/field validation, accounting consistency checks, format checks, and confidence scoring. Values that remain uncertain will be flagged for review instead of being guessed. I’d also preserve source-to-output traceability so extracted figures can be verified against the original documents. I recommend starting with the required pilot sample, measuring actual accuracy against your manual verification, then refining the extraction rules before processing the full dataset. Accuracy and data integrity will be prioritized throughout the project. Regards, Rofeal

@juanestebana17

Hello! I understand you need accurate extraction of financial statement data from scanned PDFs into your Excel panel dataset, including company/year, line-item mapping, comparative-year values, auditor details, audit date, and source filename. I have spent the last several years solving exactly this type of problem with Python, OCR, Data Processing, Data Entry, and Excel workflows for document-heavy datasets. I would use a combination of OCR/document AI plus Python validation to compare current-year and comparative-year columns, reconcile totals, and flag unreadable values for review instead of guessing. My process is built to reach 98%+ accuracy through structured extraction, cross-checks, and manual QA on low-confidence fields. I’m ready to get started, and I’m confident we can make this project a success and deliver it to a high standard. I’m committed to ensuring everything is completed professionally, efficiently, and exactly according to your requirements. Best regards!

@adriank06

I will process your 800 scanned financial PDFs using a custom Python pipeline combining OpenCV (for deskewing and cleaning the messy scans) and an advanced OCR model like Google Document AI or Tesseract. To guarantee the 98%+ accuracy required for your academic research, I will write validation scripts (e.g., automatically checking if total assets equal liabilities plus equity) and flag any uncertain Arabic or English text for manual review. Could you please send 2-3 sample PDFs so I can run the required pilot test and provide an exact quote for the full batch? I am an AI and Data Engineering Specialist with a 5.0 rating, and I have successfully delivered similar OCR and data extraction projects. You can view my portfolio directly on my profile to see my previous work. My initial price estimate is flexible and will be finalized after the pilot test. I will also provide free support after delivery to ensure the panel dataset perfectly fits your Excel template. Let's chat to start the pilot test.

@tyronet2

You need 800 inconsistent Arabic and English financial-statement scans converted into a traceable panel dataset with field-level accuracy above 98%, not unchecked OCR output. For SynapseIQ, I built document-processing and RAG pipelines focused on improving extraction accuracy across varied file formats. I’ll use Python, OpenCV, OCRmyPDF, and Azure Document Intelligence or Google Document AI after benchmarking them on the pilot. Pages will be rotated, deskewed, denoised, classified, and extracted with coordinates and confidence scores. A mapping layer will normalize account labels while preserving source text, current year, comparative year, company, auditor, report date, and period. Validation will include accounting-equation checks, subtotal reconciliation, year consistency, duplicate detection, confidence thresholds, and manual review of flagged values. Every Excel value will remain traceable to its PDF and page.