Documents Processing
Improve your document analysis models through rigorous processing and custom data annotation. We help you structure, extract and enrich your raw data to make your documents a gold mine for AI
Our experts transform your documents thanks to a advanced OCR mastery And annotation tools. Result: reliable data, ready to boost the performance of your AI models
Extraction and structuring of documents
Linguistic and multilingual treatment
Classification of documents
Supervision and human validation
Extraction and document structuring
We transform your documents into strategic resources thanks to human and technological expertise adapted to each sector.
Annotating documents
Identify, mark and qualify areas of interest (entities, sections, fields...) in various documents (PDF, contracts, forms, reports) to make them usable by AI models. This annotation can be semantic, structuring or sectoral.
Identification of the key elements to be annotated (dates, amounts, names, titles...)
Document segmentation (areas, pages, blocks...)
Manual annotation using adapted tools
Export in a structured format (JSON, XML, COCO, etc.)
Invoices — Identification and annotation of key fields (VAT, total, supplier) for accounting automation
Contracts — Marking critical clauses (termination, commitment, obligations) in complex contracts
Medical reports — Annotation of clinical segments (diagnosis, history, treatments) to structure the document
Extracting key data
Identify and extract the essential information contained in various documents (invoices, contracts, forms, statements...). To transform semi-structured or unstructured files into ready-to-use data, usable in business tools, databases or AI pipelines.
Preparation of the document (OCR if necessary, parsing according to the format: PDF, image, scan...)
Detection of target blocks or fields (text areas, tables, paragraphs, form areas)
Cleaning and structuring of extracted data (normalization, typing, enrichment)
Export in a structured format compatible with systems (JSON, CSV, XML...)
Bank statements — Automated extraction of amounts, dates and beneficiaries for audit or KYC
Customer files — Retrieval of personal data and contractual references for integration into the CRM
Survey forms — Extraction of answers or fields filled in for statistical analysis or visualization
Recognizing handwritten areas
Detect and transcribe items written manually in scanned documents (paper forms, PDF annotations, letters, etc.), in order to integrate them into databases or automatic processing pipelines. It is based on techniques combining Specialized OCR and human validation, especially in cases where writing is difficult to read.
Manual detection of handwritten areas in documents
OCR review and manual correction of the transcripts obtained
Encoding in usable formats with localization if necessary (bounding box, page, line)
Export in a standardized format according to the end use (JSON, CSV, TXT...)
Administrative letters — Recognition of dates, signatures or annotations written by hand
Handwritten fields for slips — Extraction of remarks, quantities or codes from logistics documents
Paper medical forms — Transcription of handwritten comments into patient records
Structuring complex documents
Segmenting, prioritizing, and tagging long, composite or poorly formatted documents (annual reports, contracts, regulatory files, etc.), in order to facilitate access, analysis or automatic processing.
Logical segmentation of the document into blocks of meaning (summaries, clauses, graphs, chapters)
Tag or label for each segment (type, function, hierarchical link)
Indexing or structuring content to facilitate research or AI training
Export in a suitable hierarchical format: JSON, XML, Markdown, etc.
Regulatory reports — Automatic division into chapters, annexes and regulated sections
Market studies PDF — Logical organization of segments (summary, data, graphs, analysis)
Regulatory reports — Automatic division into chapters, appendices and sections
Standardization of input formats
Transform heterogeneous documents (native PDFs, scans, images, Word...) into files standardized, cleaned and homogeneous, in order to guarantee their compatibility with automatic processing tools (OCR, extraction, classification, annotation...).
Analysis of format variations in the corpus (resolution, file type, orientation, encoding...)
Visual or structural cleaning of documents (adjustment, removal of artifacts, standardization of margins)
Renaming and logical classification of files according to a defined standard (by batch, by category, by customer, etc.)
Export to a directory or system in accordance with the business pipeline or AI
International customer documents — Standardization of the layout and expected fields
Contractual scans — Straightening and cleaning scanned documents to facilitate automatic reading
Heterogeneous PDF corpora — Standardization of resolutions, encodings and formats for OCR processing
Raw document processing
Take care of source files unstructured or difficult to use (scans, PDF captures, images, composite documents), in order to convert them into readable, segmented and usable content by automatic processing, analysis or AI systems.
Identifying the type of raw document (image-only scan, PDF without text layer, mobile capture, etc.)
Segmentation of content into usable areas (paragraphs, tables, headers, fields...)
Structuring content according to business needs (extraction, annotation, indexing)
Manual verification on a set of critical documents
Mixed business files — Processing of composite documents (forms, notes, images) for AI use
Digitized paper archives — Conversion of scanned folders into AI-readable OCR files
PDF captures without text — Extraction of useful areas via visual segmentation then OCR
Treatment linguistics
We transform your documents into strategic resources thanks to human and technological expertise adapted to each sector.
Multilingual treatment
Manage written or audio documents in different languages — including languages that are unusual or have a strong cultural context — in order to prepare for extraction, annotation, or translation. This step ensures a consistent, fair, and robust support in multilingual AI projects.
Automatically or manually detect the language of the document
Involve a native speaker or a specialized annotator
Transcribe or translate multilingual segments while maintaining the original structure
Encode data with linguistic metadata (language, register, level)
Multilingual NLP corpus — Prepare balanced datasets to train or test models in multiple languages
International contracts — Structure multilingual legal documents for extraction or review
Multi-regional customer formsX — Process customer data in several languages for analysis or automatic response
Transcription and human translation
Call on qualified speakers to accurately transcribe audio or video files, or to translate multilingual documents. Unlike fully automated approaches, this method allows manage nuances, correct errors, and produce reliable data for AI models or critical uses.
Divide documents or audio files into usable segments
Manually transcribe words or texts, respecting punctuation and the specificities of the spoken language
Translate content into the target language, with attention to tone, register, and context
Structure the results (bilingual file, timestamp, metadata) and export them in the desired format
Audio datasets for NLP — Produce validated audio/text corpora for voice recognition or machine translation
Interviews or podcasts — Transcribe and translate recordings to create multilingual AI datasets
Interviews or podcasts — Transcribe and translate recordings to create multilingual AI datasets
Multilingual annotation
Annotate documents or transcripts in different languages by adding semantic, syntactic, or functional information. This step is required to train or test natural language processing (NLP) models able to understand and deal with a great deal of linguistic diversity.
Select the target languages and the types of annotation to be applied (named entities, emotions, intentions...)
Prepare the documents or segments to be annotated, taking into account the specificities of each language
Apply annotations in appropriate interfaces (plain text, audio files, transcripts)
Export annotated data in a format compatible with multilingual models (JSON, CSV, XML...)
Multilingual NLP corpus — Annotate entities or intentions in multiple languages to train multilingual LLMs
Annotated translations — Provide source-target pairs enriched with semantic tags for neural translation
International voice assistants — Annotate audio or text dialogues in several languages to understand intent
Human proofreading and validation
Involve linguistic experts or specialized annotators to check, correct and validate content derived from transcriptions, translations or automatic processing. This step eliminates errors, unifies styles, and ensures compliance with project or domain requirements (legal, medical, administrative...).
Proofread content transcribed by AI, translated or annotated line by line or block by block
Correct mistakes, approximations or inconsistencies (grammar, style, terminology, punctuation...)
Validate or invalidate each element according to defined criteria
Documenting the types of errors encountered to improve the upstream steps
Transcript corpus — Correct punctuation, spelling, or cutting errors in automatically generated texts
AI training games — Manually validate AI responses or transcripts to constitute a reference corpus
Technical translations — Verify terminological consistency in specialized documents
Validation of extracted data via OCR
Have texts generated by optical character recognition (OCR) from scanned or photographed documents read and corrected manually. This step is essential to guarantee the reliability, completeness and usability data before being used by AI systems or in business flows.
Collect raw OCR results (text, structure, spatial coordinates)
Read OCrised snippets line by line or block by block
Correct typographical errors, truncated words, poorly recognized characters
Export the corrected data in a structured format (rich text, JSON, XML...) compatible with later uses (rich text, JSON, XML, etc.)
Digitized paper archives — Verify the readability and accuracy of OCR extracts to build historical corpora
Regulatory files — Validate the compliance of OCR extracts for audit or administrative submission
Bank bills or statements — Correct recognition errors in amounts, numbers or names
Evaluation of transcripts and AI translations
Compare automatically generated content (by transcription or translation models) to human references, in order to measure their precision, fluidity, fidelity to the original meaning and contextual adequacy. This step makes it possible to calibrate the models, detect weaknesses and create reliable test sets.
Collect AI results (transcripts or translations)
Define evaluation criteria (fidelity, grammar, style, style, consistency, critical errors...)
Conduct a comparative human evaluation (scoring, ranking or qualitative comments)
Document significant differences and their causes (poor segmentation, counter-meaning, hallucination...)
Voice transcription templates — Note the accuracy of the transcriptions in context (noise, accents, interruptions...)
Specialized AI systems — Verify that translations respect business terminological constraints (health, legal, technical)
Multilingual test corpus — Evaluate the quality of translations in several languages to prioritize improvements
Classification of documents
We transform your documents into strategic resources thanks to human and technological expertise adapted to each sector.
Manual sorting of documents
Involve annotators for Filing raw documents in defined categories (e.g. contract, invoice, report, identity document...), according to their content, structure or use. This step allows prepare coherent corpora for the training or validation of automatic classification models, or for direct exploitation by business teams.
Upload documents into a suitable annotation interface (PDF, images, scans...)
Manually assign one or more labels per document
Check the coherence between annotators (business rules, ambiguous cases...)
Export the results (file + associated category) in a structured format (CSV, JSON)
Automated archiving — Create a classified data set to train an automatic sorting model
Regulatory treatment — Identify regulated documents to be isolated or treated as a priority
Raw documentary corpus — Classify files according to their type (invoice, contract, pay slip...)
Verification of AI classifications
Manually review the predictions made by a document classification model to validate or correct the assigned categories. This ensures high precision in automated sorting systems, build reliable validation data sets, or to generate useful feedback for improving the model.
Review the content of each document to verify automatic classification
Accept or correct the label proposed by the template
Mark problem documents (lack of info, noise, mixed content...)
Export verified results for performance analysis or re-training
Continuous improvement — Correct erroneous predictions to retrain a more efficient model
Classification model audit — Verify the real accuracy of an AI classifier on a business corpus
Reliability of an automated pipeline — Integrate a human step into a critical sorting process
Labelling of documents
Assign one or more labels to files according to their nature, content or business objective. This step is essential to create supervised training datasets for automatic classification or sorting models, or to generate a ground truth (”Ground Truth“) used during the test or evaluation phase.
Define a clear and consistent set of classes or labels
Upload the documents to be annotated in a suitable tool (Label Studio, Doccano, internal tool...)
Annotate accurately, according to defined instructions
Export annotated documents with tags in a structured format
Benchmarking — Create a ground truth to test the performance of a model on real cases
Documentary organization — Structure a large volume of documents to facilitate their business exploitation
AI classifier training — Produce an annotated corpus to learn to recognize the types of documents
Document segmentation
Identify and separate the different parts of a composite document (e.g.: report, contract, administrative file), in order to classify each segment independently, or to extract the relevant areas for annotation, extraction or AI processing.
Segment the file manually or semi-automatically (page by page or block by block)
Annotate each segment with a label or associated type
Check the consistency of the cut segments (order, completeness, typing)
Export segments in separate files or in a structured format with their metadata
Regulatory reports — Automatically cut sections (summary, analysis, appendices) for targeted treatment
Complex contracts — Extract and classify clauses, conditions and appendices for annotation or extraction
Customer or HR files — Identify individual parts within a global PDF
Add metadata
Associate to each document or segment descriptive, technical, or contextual information (type, date, language, origin, level of sensitivity...). This metadata makes it possible toimprove search, classification, and document management or even the training of better informed AI models.
Define the types of metadata useful according to the objectives of the project (ex.: typology, source, confidentiality...)
Enter or select metadata using an annotation tool or a manual grid
Link metadata to documents in the target format (via built-in fields, or in an external database)
Export rich files (JSON, CSV, database or documentary index)
Preparing AI datasets — Provide additional guidance to models to refine predictions
Business documentary databases — Enrich files with business categories, key dates or thematic tags
Smart search tools — Improve archival filtering and navigation through rich metadata
Qualitative cleaning
Manually review and filter a set of documents in order to delete noisy, incomplete, irrelevant, irrelevant, duplicate, or unusable files. This step ensures that only relevant, legible, and useful documents are kept in a corpus intended for training a model or for reliable classification.
Define exclusion criteria (image quality, empty content, bad language, duplicates, irrelevant...)
Browse documents in a quick review or annotation tool
Mark non-compliant files according to their exclusion reason
Document the reasons and volumes of rejection for traceability or improvement of sourcing
Cleaning corpus collected on the web or in business — Eliminate parasitic or useless documents
Preparing for annotation — Guarantee a clean and coherent corpus before launching a labelling phase
Composition of an AI training game — Remove unclear, out-of-domain, or poorly scanned documents
Supervision and human validation
We transform your documents into strategic resources thanks to human and technological expertise adapted to each sector.
Manual check of extracted data
Involve human reviewers to validate or correct the automatically retrieved data from documents (e.g. amounts, dates, names, technical fields). This step allows you to making structured data reliable, especially in sensitive or regulated contexts.
Identifying the import source documents and their extracted data (by OCR or parsing) objects to be annotated
Correct detected errors (truncated words, erroneous amounts, poorly recognized entities,...)
Mark ambiguous or unusable cases
Export the reviewed data in a structured format (CSV, Excel, database)
Product sheets or catalogs — Control the technical fields resulting from automatic parsing
AI test corpus — Produce 100% verified data to train or evaluate a model
Invoices or contracts — Verify that the amounts, dates and stakeholders extracted are accurate
Manual OCR or parsing adjustment
Intervening directly on the results of an automated extraction (OCR text, HTML or XML parsing, PDF extraction) in order to rectify localized errors, such as poorly recognized words, poorly segmented lines, or poorly associated fields. This targeted intervention significantly improves the overall quality of the extracted data.
Identify documents or segments with recognition errors
Manually correct detected errors (truncated texts, inverted fields, merged paragraphs...)
Realign poorly positioned or typed segments
Export adjusted data in a format compatible with the rest of the corpus
Parsing complex PDFs — Reassociate the right labels with incorrectly extracted tables or paragraphs
Scanned forms — Realign OCrised fields with the original labels
OCR on technical documents — Correct poorly segmented lines or poorly recognized symbols
Proofreading documents
Reread in whole or in part documents extracted, transcribed or processed automatically in order to correct errors, validate the layout, or detect anomalies. This step makes it possible to guarantee a linguistic, technical or regulatory quality before distribution, archiving or annotation.
Upload the original documents and their processed version (OCR, parsing, transcription,...)
Correct content, style, or structure errors (errors, misordered segments, repetitions)
Validate or reject documents according to defined quality criteria
Documenting common mistakes to adjust early steps
Corpus IA — Review annotated or extracted documents before model training
Structured archiving — Verify that the extracted documents are legible, complete and usable
Regulatory documents — Review and correct transcripts for audit or official submission
Sensitive data tagging
Detect, annotate, or hide the elements of a document containing personal, confidential or regulated information (PII, health data, legal notices, etc.).
Define the types of sensitive data to be identified (name, number, number, address, ID, medical data...)
Load textual, transcribed, or OCR documents into an annotation tool
Apply tags, masks, or anonymizations according to project rules
Export the document annotated, pseudonymized, or ready for AI training
Preparing datasets for LLM — Delete or tag personal information before training
Treatment of HR or medical files — Identify sensitive mentions for pseudonymization or audit
Regulatory compliance — Guarantee compliance with the RGPD or sector standards (e.g.: HIPAA, AI Act)
Test data for OCR/NLP
Manually select, correct and validate representative documents or extracts, in order to make them test data for the measure of accuracy, robustness, and errors to assess models capability in recognizing or understanding documents
Select a diverse and representative sample of documents or use cases
Apply a very high quality manual annotation
Compare the AI results to this reference to calculate scores (precision, F1, CER, etc.)
Document the types of errors observed to guide corrections or fine-tuning
Multilingual NLP model testing — Measure performance by language or by type of document
Quality monitoring in AI pipelines — Regularly monitor the drifts or regressions of a system in production
OCR engine evaluation — Compare the automatically extracted text to a 100% proofread version
Automatic cutting correction
Manually check and adjust the cuts made by an automatic segmentation system (e.g.: OCR, PDF parsing, detection of blocks or pages).
Upload documents and their initial breakdown into a review or annotation interface
Merge, split, or reorder segments according to the expected logical structure
Validate the consistency of the reconstructed document
Export the corrected file with its updated structure (JSON, XML, etc.)
Extracted tables — Correct the separation of columns or rows in financial documents
Scanned forms — Readjust misaligned blocks to allow reliable annotation or extraction
PDF contracts or reports — Reorder sections misinterpreted by an OCR or parsing tool
Use cases
Our expertise covers a wide range of AI use cases, regardless of the domain or the complexity of the data. Here are a few examples:
Why choose
Innovatiana?
We put at your service a rigorous and adaptable team of experts, specialized in structuring, revision and enrichment of documentary corpora, to feed and optimize your AI models
Our method
A team of professional Data Labelers & AI Trainers, led by experts, to create and maintain quality data sets for your AI projects (creation of custom datasets to train, test and validate your Machine Learning, Deep Learning or NLP models)
We offer you tailor-made support taking into account your constraints and deadlines. We offer advice on your certification process and infrastructure, the number of professionals required according to your needs or the nature of the annotations to be preferred.
Within 48 hours, we assess your needs and carry out a test if necessary, in order to offer you a contract adapted to your challenges. We do not lock down the service: no monthly subscription, no commitment. We charge per project!
We mobilize a team of Data Labelers or AI Trainers, supervised by a Data Labeling Manager, your dedicated contact person. We work either on our own tools, chosen according to your use case, or by integrating ourselves into your existing annotation environment.
You are testifying
In a sector where opaque practices and precarious conditions are too often the norm, Innovatiana is an exception. This company has been able to build an ethical and human approach to data labeling, by valuing annotators as fully-fledged experts in the AI development cycle. At Innovatiana, data labelers are not simple invisible implementers! Innovatiana offers a responsible and sustainable approach.
Innovatiana helps us a lot in reviewing our data sets in order to train our machine learning algorithms. The team is dedicated, reliable and always looking for solutions. I also appreciate the local dimension of the model, which allows me to communicate with people who understand my needs and my constraints. I highly recommend Innovatiana!
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Innovatiana helps us to carry out data labeling tasks for our classification and text recognition models, which requires a careful review of thousands of real estate ads in French. The work provided is of high quality and the team is stable over time. The deadlines are clear as is the level of communication. I will not hesitate to entrust Innovatiana with other similar tasks (Computer Vision, NLP,...).
Several Data Labelers from the Innovatiana team are integrated full time into my team of surgeons and Data Scientists. I appreciate the technicality of the Innovatiana team, which provides me with a team of medical students to help me prepare quality data, required to train my AI models.
Innovatiana is part of the 4th promotion of our impact accelerator. Its model is based on outsourcing with a positive impact with a service center (or Labeling Studio) located in Majunga, Madagascar. Innovatiana focuses on the creation of local jobs in areas that are poorly served or poorly served and on transparency/valorization of working conditions!
Innovatiana is deeply committed to ethical AI. The company ensures that its annotators work in fair and respectful conditions, in a healthy and caring environment. Innovatiana applies fair working practices for Data Labelers, and this is reflected in terms of quality!
In a context where the ethics of AI is becoming a central issue, Innovatiana shows that it is possible to combine technological performance and human responsibility. Their approach is fully in line with a logic of ethics by design, with in particular a valuation of the people behind the annotation.
Working with Innovatiana has been a great experience. Their team was both reactive, rigorous and very involved in our project to annotate and categorize industrial environments. The quality of the deliverables was there, with real attention paid to the consistency of the labels and to compliance with our business requirements.
Innovatiana embodies exactly what we want to promote in the data annotation ecosystem: an expert, rigorous and resolutely ethical approach. Their ability to train and supervise highly qualified annotators, while ensuring fair and transparent working conditions, makes them a model of their kind.
🤝 Ethics is the cornerstone of our values.
Many data labeling companies operate with questionable practices in low-income countries. We offer an ethical and impacting alternative.
Stable and fair jobs, with total transparency on where the data comes from
A team of Data Labelers trained, fairly paid and supported in its evolution
Flexible pricing by task or project, with no hidden costs or commitments
Virtuous development in Madagascar (and elsewhere) through training and local investment
Maximum protection of your sensitive data according to the best standards
The acceleration of global ethical AI thanks to dedicated teams
🔍 AI starts with data
Before training your AI, the real workload is to design the right dataset. Find out below how to build a robust POC by aligning quality data, adapted model architecture, and optimized computing resources.
Feed your AI models with high-quality, expertly crafted training data!
