AI-Based Data Extraction from Documents

@Hrolgar

Before any code there's a choice to make. I can match headings against a fixed dictionary built from your sample reports, which is predictable but misses a lab that words things differently. Or I can let the model read each heading freely, which catches 'Natural Bulk Density' by itself but can drop it in the wrong column. I'd take the dictionary and have the model propose matches against it. Values go in through openpyxl so formatting, formulas and logos stay put. Doubtful numbers get a confidence flag for review instead of being entered quietly. I haven't tried it on your scans yet, so M1 is where I find out how they behave. M1: field dictionary plus digital PDFs, 15840 INR, 3 days M2: scans and images, template filling, 16000 INR, 4 days M3: confidence flags, review sheet, handover, 16160 INR, 3 days Total 48000 INR, 10 days. That's a starting number off the brief, and I'll firm it up once I know how many sheets and fields the template has. It holds if hosted vision/LLM APIs do the reading (no training or GPU), you pay the API usage, there's one template family with 5-10 sample reports and their finished workbooks, and the conversion rules are written down or visible in those workbooks. If any of that differs, I re-quote before starting. Are your bore logs mostly typed or handwritten?

@mayankinnovative

You require a context-aware AI document understanding pipeline—not merely simple OCR-to-Excel—capable of mapping engineering/geotechnical data to the correct Excel fields by comprehending the semantic relationships between headings, tables, and values ​​across varying layouts. Maintaining high accuracy and preserving existing template formatting will be the core priorities. My Approach: I will build a pipeline using Python that combines OCR and PDF/table extraction with an LLM/Document AI-based layer for field normalization and validation. I will derive mapping rules and structured prompts from sample documents and correctly completed Excel files. Using OpenPyXL, I will implement batch processing, conversion rules, and exception handling while preserving the existing workbook structure—including formulas, merged cells, formatting, and worksheets. A manual review queue can also be established to flag ambiguous values. 1. How many distinct source formats are present in the sample documents? 2. Which fields are mandatory, and which are subject to calculation or conversion rules? 3. Are cloud AI APIs restricted due to data privacy concerns? 4. Should uncertain values ​​be highlighted separately in the final workbook? I will first validate accuracy using sample-to-output comparisons, then generalize the pipeline to handle new layouts, ultimately delivering a production-ready and maintainable solution.

@UzeeTech

The hard part of your brief is not OCR, it is the semantic mapping: Bulk Density and Natural Bulk Density must land in the same Excel field even when the layout changes completely. I would design around heading understanding. I would build a Python pipeline: an OCR and layout pass over the PNG, JPEG, and PDF inputs, then an LLM-based extraction step that reads headings and nearby labels, normalizes synonyms against a mapping dictionary we refine together, and writes values into your template with openpyxl, preserving formatting, formulas, merged cells, and logos. Your samples and completed Excels become my test set: I run the pipeline against them, measure field-level accuracy, and iterate until new layouts generalize. Batch processing, error handling, and run documentation are included. Relevant experience: I built Jyotish AI, a pre-launch AI astrology app for Android and web, working with AI APIs and structured data pipelines. Two questions: roughly how many distinct report formats do your samples cover, and are the Excel templates identical across them or do they vary too? I can deliver the working pipeline, tested on your samples with source code and documentation, in 14 days for ₹33,000. Happy to start with one document type first.

@theyasirahmad

No description found.

@TeraByte26

Hi, I will create an AI‑powered pipeline that reads PNG/JPEG/PDF reports, extracts context‑aware values such as Bulk Density, SPT N‑value, soil classification, and fills your exact Excel template preserving formatting and formulas. Delivery in three weeks within 22,000 INR. I can share a free sample now—ready to start. Waiting for your response in chat! Best Regards.

@NEHABHAT92

As an enthusiastic AI developer with vast experience in web and mobile app development for over 9+ years, I believe my skills set me apart from other candidates. I have extensive experience working with Python, OCR technologies, as well as various OpenAI/LLM APIs - all of which are integral to the success of this project. Additionally, my mastery in handling PDF extraction, document AI, and computer vision align seamlessly with your project's needs. My goal is to streamline your data extraction efforts to a point where you feel like you're using a magic wand to complete your Excel sheets. Moreover, proficient in using Pandas and OpenPyXL frameworks for data manipulation, I am well-positioned to tackle the challenge of handling dissimilar data sources and populating the correct cells systematically at-scale without compromising on your template's formatting. Notably, accuracy is crucial in engineering investigations; thus, my extensive experience in mapping and fine-tuning machine learning models is ideal to ensure precise data extraction and analysis. To assure accuracy for new formats, I will derive insights from the samples provided by you but will build a future-ready solution that can adapt to evolving formats over time without needing your intervention. My work doesn't end at just delivering a working Python code; instead, it encompasses building an entire AI/OCR/document-processing pipeline that can handle batch processing effectively. Partnering chave su

@digitaldotsent

The real challenge here is that your bore logs and lab reports come from different labs with inconsistent layouts, so a fixed position OCR script will break the moment Bulk Density moves from row 5 to row 12 or gets relabeled as Natural Bulk Density. You need semantic field matching, not coordinate scraping. My approach would be a Python pipeline using OCR such as Tesseract or a cloud OCR for scanned pages, plus PDF text and table extraction for digital PDFs using pdfplumber or Camelot for tables. Extracted text and table structures would then go through an LLM, GPT or similar, with a defined schema of your target fields like Bulk Density, SPT N value, fines percentage, depth, soil classification, so it maps synonyms and varying phrasing to the correct canonical field regardless of position. Once values are normalized, I would use openpyxl to write into your existing Excel template, preserving formulas, merged cells, formatting, logos and borders, and handling sheet creation or renaming as needed. Conversion and calculation rules would be applied as a config step before writing. Could you share two or three sample reports with differing layouts along with your target Excel template so I can confirm the field mapping accuracy before we start. Syed, Digital Dots

@recomsoft

Hi, I'd build this as an LLM-based extraction pipeline, not positional OCR: 1. Ingest: digital PDFs via pdfplumber; scans/PNG/JPEG via a vision LLM (GPT-4o / Claude) or Azure Document Intelligence for complex tables. 2. Understanding: the model gets a field dictionary (Bulk Density, SPT N, fines %, depth, soil classification...) with synonyms built from your sample reports + completed Excel files, and returns strict JSON: value, unit, source page. 3. Validation: unit conversion and your calculation rules in Python, range checks, and a "needs review" list for missing/unclear values - nothing is guessed silently. 4. Excel: openpyxl writes into your template (create/copy/rename sheets) and keeps formulas, merged cells, logos and formatting. You get: a batch tool, source code, an editable mapping config (add a new synonym without touching code), a run guide, and an accuracy report per field on your samples. Fine-tuning is usually not needed at first - few-shot prompting from your examples generalises better across varied layouts. Free sample: send 2 reports + their completed Excel and I'll return the filled sheet before you award. Question: is it one Excel row per sample depth, or one sheet per borehole? Nurlan

@vitaliiy4

Hello! Your key requirement is clear: the tool must understand what a value means ("Bulk Density" vs "Natural Bulk Density"), not read fixed positions, and it must fill your existing Excel template without breaking it. How I would build it: 1. Read the documents: PDF text layer when it exists, OCR for scans and photos, table detection for test tables (bore logs, lab reports, SPT N-value, fines percentage, depth, and so on). 2. Understand and map: an LLM reads the extracted text and tables and maps every value to a canonical field by meaning, with a synonym list that we grow from your real reports. Unit conversions and calculation rules are applied in code. Low-confidence values are flagged for review instead of being guessed. 3. Fill Excel: openpyxl writes only into the target cells of your template, so formatting, formulas, merged cells, logos and borders stay untouched. Output is a copy of the template per batch of documents. Price in the field is INR 30,000 for a working version: a Python tool (command line or simple upload page), tested on your sample reports, with a short usage note. Timeline is 14 days after I receive samples. I will tune the extraction rules on your documents until the key fields are accurate on them. One question: can you share 3-5 sample reports from different sources and the Excel template? Also, are the documents confidential, so that I must use a local model instead of an external API?

@JessyM29200

Hi, I'd build the mapping from your own completed files. They become the field list, with every name each lab uses for a value, so Bulk Density and Natural Bulk Density land in one cell. They're also my answer key for testing. The tool is Python. Digital PDFs are read as text; scans and photos go through OCR and a vision model. An AI model fills a fixed field list with each value's unit and source line, and leaves a field empty rather than guess. Code applies your conversion rules and range checks, then fills a copy of your template. My first test proves formulas, merged cells and logos survive on your file. Anything unclear goes to a review list. First accuracy report within a week; code and guide by day 14. I've built this pattern for invoice PDFs, and a two-page plan for your reports is ready to share once you open a chat. When a value is missing, should its cell stay blank or carry a flag?

@gavinm11

Hi, this is a good fit for an LLM-based extraction pipeline rather than position-based OCR, and the "Bulk Density" vs "Natural Bulk Density" example is exactly the problem it solves. Proposed approach: 1. Ingest: digital PDFs are read with pdfplumber (text + tables, no OCR loss); PNG/JPEG and scanned PDFs go to a vision-capable LLM (Claude or GPT-4o, your choice of API key) or Azure Document Intelligence if you prefer a Microsoft stack. 2. Field schema from your examples: I build a mapping file (YAML) from your completed Excel samples, listing each target field (SPT N-value, depth, bulk density, fines %, soil classification...), its synonyms, units and its target sheet/cell or column. New synonyms are added by editing that file, not code. 3. Extraction: the model returns strict JSON for that schema with a source quote and page for every value, so each number is traceable. Units are normalised (g/cc, kN/m3, %) with your conversion rules. 4. Validation before writing: range checks per field (e.g. bulk density 1.2-2.6 g/cc), depth ordering per borehole, missing-value flags. Anything doubtful goes to an "Exceptions" sheet instead of silently into the template. 5. Excel writing with openpyxl into your existing template, preserving formulas, merged cells, logos, borders and print layout; copying/renaming sheets per borehole where needed. 6. Batch mode: drop a folder of files, get one completed workbook per report plus a run log. 7. Testing: I measure field-level accuracy against your completed Excel samples and report the numbers before handover. No fine-tuning needed at first; few-shot examples from your samples usually get there, and I would only propose fine-tuning if the measured accuracy says so. Deliverables: Python source, requirements file, the mapping file, a README with run instructions, and the accuracy report on your samples. Timeline about 10 days after receiving samples. Full disclosure: I use AI-assisted coding tools and review and test everything myself. Happy to run 2 of your sample reports through a first prototype for free so you can see the extraction quality before awarding. Gavin

@AhmedHashimPro

Hi, I'm a software architect with 11 years of experience, and most of my recent work is Python and LLM pipelines. I built My Stream, an AI publishing platform that runs on 7 LLM providers, and my public GitHub (ahmed-hashim-pro) has rag-knowledge-agent and llm-contract-router, which validates LLM output against a strict schema. That last part is what matters here: the model must return the right field or flag it, never guess. How I would build it: 1. OCR and table extraction for scans and images, direct text for digital PDFs. 2. An LLM step that maps what it reads to your fixed field list (so "Natural Bulk Density" and "Bulk Density" land in the same field), with units and a confidence value per field. 3. OpenPyXL writes into your existing template, touching only the target cells so formulas, merged cells and formatting stay intact. 4. Batch mode, plus a review sheet listing low-confidence or missing values for a human check. 5. I test against your sample files and completed Excel sheets and report accuracy per field. To be honest, I am not a geotechnical engineer, so I will rely on your samples and field list for the domain rules. Questions: - Roughly how many distinct fields, and how many documents per batch? - Can the documents be sent to a cloud LLM/OCR API, or must it run offline? - Are any values handwritten? Best, Ahmed

@aiinno

As an experienced AI and Backend Engineer specialising in RAG-based document understanding and Computer Vision, I bring expertise in building scalable, production-ready AI solutions. With experience at companies such as Google, eBay, Deutsche Bank, InMobi, and Blinkit, I have developed strong skills in enterprise systems, distributed architectures, and AI-powered automation. I understand the challenges of extracting structured information from diverse documents with varying layouts, including scanned and digitally generated PDFs, PNGs, and JPEGs. My expertise in OCR, LLMs, and intelligent data extraction enables me to build reliable solutions that accurately identify, extract, validate, and organise information. I can leverage OpenAI APIs, Python, Pandas, OpenPyXL, and cloud services such as Azure Document Intelligence, Google Document AI, and AWS Textract to develop an efficient extraction pipeline tailored to your requirements. For engineering and geotechnical investigation documents, accuracy and data integrity are critical. I can implement validation mechanisms, confidence scoring, exception handling, and structured Excel/CSV exports to minimise errors and support reliable analysis and decision-making. My approach focuses on understanding your requirements, testing representative documents, and continuously improving extraction accuracy. I am confident in delivering a robust, scalable, and maintainable solution for your project.

@tyagi5011

Hi, I'm Nishant Tyagi, a developer with 6 years of experience in Flutter, React Native, Kotlin, iOS and PHP/Laravel, building production mobile apps and web platforms, including AI-powered and e-commerce solutions. For this project I will deliver AI-based data extraction from documents using OCR and LLMs, with structured output, validation and an easy review UI. Why me: - 6 years of hands-on delivery across mobile, backend and APIs - Clean, well-documented, scalable code - Fast communication and regular progress updates - Post-delivery support included I can start immediately and deliver in 7 days. Let's discuss the details so I can begin right away. Best regards, Nishant Tyagi

@koussedia

Hi — you need PNG/JPEG/PDF documents turned into structured Excel data automatically. I build exactly this: an OCR pipeline that reads documents, extracts the key fields, and fills a clean Excel register — see my portfolio video for a working demo (invoice to Excel, end to end). You'll get: a working extraction pipeline tuned on your own document samples, tested before delivery, plus a short guide so your team can run it. Timeline: 5-7 days including testing on your real files. My bid: $280 fixed. Quick question: roughly how many documents per month — and are the layouts consistent or varied?

@MrShortFlow

The core problem here is semantic mapping, not OCR: the same field (e.g. Bulk Density) appears under different labels, positions and table layouts across reports, and still has to land in the one fixed Excel cell without breaking formulas or formatting. Delivery: a Python pipeline (OCR/table extraction + heading-aware matching against your sample files) that reads PNG/JPEG/PDF, normalises label variants, maps values to the correct worksheet/column via openpyxl while preserving merged cells, formulas, borders and logos, and applies your conversion rules before writing output. v1 within 2 days once I have your sample files + completed Excel templates, covering the formats you send; remaining edge cases refined by day 5. Here is a normalisation rule, done now: IF label MATCHES r"(natural\s+)?bulk\s+density" (case-insensitive) THEN field = "Bulk Density", value = first numeric token before "g/cc", write to template cell mapped to that field — regardless of source position or exact wording. I do not promise 100% automatic accuracy on documents with no example provided — these get flagged for your review rather than silently guessed. Can you share 2-3 sample source files with their correctly filled Excel counterparts to start mapping? Price: 18800 INR fixed · Timeline: 5 working days from access/brief Konrad

@VividusDesigns

Hi, I have 8+ years of AI/LLM document automation and full-stack development experience across Python, OCR, OpenAI/Claude APIs, Azure Document Intelligence and OpenPyXL. For your geotechnical reports I will build a Python tool that OCRs PNG, JPEG and scanned or digital PDFs, pulls out tables, and uses an LLM with a synonym and field map to recognise values like Bulk Density, SPT N-value, fines % and depth wherever they appear. Values are validated, unit-converted and calculated by your rules, then written into your existing Excel template with OpenPyXL so formulas, merged cells, logos and layout stay untouched. I will tune it against your sample files and completed sheets and give you a review screen for low-confidence fields. I'll also provide full source code, a simple batch runner, setup documentation and post-delivery support. I can make it with a big smile, promise. Please DM me for further discussion. Thanks, Vivek Tailor

@odinbrs

Hi! Since accuracy matters more than speed here, I'd build it so every value can be checked: - PDFs with text: tables and fields read directly (pdfplumber). Scans and photos: OCR with layout, then the same step. - A vision-capable LLM maps what it reads to your Excel fields by meaning, using a synonym dictionary built from your samples ("Bulk Density" = "Natural Bulk Density"), plus your unit and calculation rules in code. - Every extracted value keeps its source file, page and a confidence level. Anything missing or unclear goes to a review sheet; it is never filled in silently. - openpyxl writes into your template without touching formulas, merged cells, logos, borders or layout; it creates or renames sheets where your rules say so. Batch mode for folders. - Accuracy measured field by field against your completed Excel samples, and the report is part of the delivery. First stage: INR 37,000, 14 days from the funded milestone, covering your sample document types and template. More formats or a desktop interface can come after. Questions: how many samples and fields? Can documents be sent to a cloud AI API (OpenAI or Azure), or must everything run locally? Monthly volume? I lead and review the work; AI tools do part of the implementation under my direction. API usage costs are billed to your own account.

@ronakkataria1

Your Bulk Density example has a flip side. The matching that rightly joins 'Bulk Density' and 'Natural Bulk Density' will just as happily drop 'Dry Density', or a Proctor test's 'Maximum Dry Density', into the same cell. All are in g/cc and close enough to pass a range check. SPT has the same problem with observed and corrected N. A model that always returns something fills your template with confident wrong numbers, and nobody spots them until the analysis is off. So matching runs on a field dictionary built from your completed Excel samples: labels that count, labels that must never match, the unit and a sane range. Azure Document Intelligence or AWS Textract reads scans and tables, an LLM proposes each value with its page and source line, and the dictionary decides. Anything unsure stays blank and goes to a review sheet. An empty cell beats a guessed one. Unit conversions like kN/m3 to g/cc run in code, never in the model. On training: with a few dozen samples, fine-tuning isn't step one. Your completed Excel files become the test set, and you get accuracy per field against them. The template is filled cell by cell and compared before and after, so formulas, logos and layout don't move. I'm not a geotechnical engineer, so you sign off the dictionary. Background: NLP document AI, OCR work, RAG pipelines on GPT-4, LLaMA and Claude. AWS and Azure certified. My rating here is 5 out of 5. How many different labs or report layouts are in your samples? Ronak