Discover i10X’s curated AI OCR tools for turning images, scanned PDFs, receipts, forms, and handwritten notes into editable, searchable text—built for faster document digitization, multilingual extraction, and workflow automation.
Juggling three separate OCR tools ate 12 hours a week; i10X collapsed our invoice digitization into one free agent and cut processing time 70%.
Time saved weekly12 hrs
Priya Sharma
Operations Manager
Our fragmented OCR stack cost $180 monthly plus endless tab-switching; i10X eliminated the spend and raised extraction accuracy from 82% to 97%.
Monthly tool spend cut$180
Alex Rivera
Indie Founder & Developer
Manual re-keying of scanned patient forms burned 20 staff hours weekly; i10X automated it free and dropped error rates by 45% overnight.
Error rate reduction45%
Jordan Hale
Accounting Lead
O que o agente pode fazer por Image Analysis
Um Superagent, com subagentes especializados para cada tarefa.
You add images or PDFs; i10X reads file type, language signals, and scan quality.
2
Set Extraction Goals
You choose text, tables, forms, or searchable PDFs; i10X configures OCR settings automatically.
3
Run Super Agent
You start the job; i10X preprocesses pages, recognizes text, and structures clean editable output.
4
Review And Refine
You check results and request fixes; i10X improves formatting, corrections, and exports.
Para quem é
Feito para as tarefas concretas que as pessoas realmente fazem.
Accounts Payable Specialist
Tarefas que o agente executa
Extract invoice numbers, vendor names, dates, totals, tax, and line items from scanned invoices.
Convert receipt images and PDF statements into editable tables for reconciliation.
Flag missing fields or low-confidence text for quick review before ERP entry.
Batch-process supplier documents into searchable files for audit trails.
Resultado: Month-end stops feeling like a paper chase: invoices become review-ready data, freeing AP to resolve exceptions instead of retyping totals.
Legal Assistant / Paralegal
Tarefas que o agente executa
Turn scanned contracts, discovery files, exhibits, and court records into searchable text.
Extract clauses, party names, dates, signatures, and case references from long legal PDFs.
Create searchable document sets for review, filing, and e-discovery preparation.
Compare OCR output in chat to correct names, citations, or formatting before use.
Resultado: Legal teams move from scanning to strategy faster, with searchable case materials ready for review, filing, and deadline-sensitive work.
Medical Records Clerk
Tarefas que o agente executa
Digitize patient intake forms, lab reports, referrals, and insurance documents from scans or faxes.
Extract patient identifiers, dates, provider details, diagnosis notes, and form fields.
Convert handwritten or low-quality medical paperwork into searchable text for chart updates.
Prepare structured summaries for review while keeping staff focused on patient support.
Resultado: Records staff spend less time decoding forms and more time keeping charts accurate, accessible, and ready for care teams.
Document Control Specialist
Tarefas que o agente executa
Convert archived PDFs, scanned manuals, compliance files, and forms into searchable repositories.
Extract document titles, revision numbers, dates, owners, and approval details.
Rename, classify, and organize batches of documents using captured metadata.
Prepare editable versions of scanned records for audits, updates, or internal workflows.
Resultado: Archives become living knowledge bases: the right file, revision, or approval detail is searchable in seconds.
Data Entry Clerk
Tarefas que o agente executa
Read text from images, PDFs, forms, business cards, and scanned spreadsheets.
Populate clean rows of names, addresses, IDs, totals, and notes from messy source files.
Detect duplicates, formatting inconsistencies, and fields needing human confirmation.
Export extracted text into copy-ready tables for CRM, spreadsheets, or back-office systems.
Resultado: Manual entry shrinks to quality control, letting operations teams move cleaner data into systems with fewer copy-paste errors.
Academic Researcher
Tarefas que o agente executa
Convert book chapters, article scans, handwritten notes, and archival materials into editable text.
Extract quotes, citations, page references, tables, and multilingual passages from research documents.
Create searchable notes from image-heavy PDFs for faster literature review.
Clean OCR text in chat so it is ready for annotation, translation, or citation.
Resultado: Research momentum improves because source material becomes searchable, quotable, and reusable without hours of transcription.
Superagent versus ferramentas isoladas
Recurso
Superagent
Ferramentas isoladas
Setup and integration effort
One agent workspace can connect OCR, document intake, extraction, validation, and downstream actions from a single setup flow.
Each OCR, storage, automation, and export tool usually needs separate setup, credentials, and testing.
Number of tools required
Uses one platform for document ingestion, OCR extraction, enrichment, routing, and reporting.
Typically combines separate OCR, PDF editor, cloud storage, spreadsheet, automation, and analytics tools.
Workflow automation coverage
Can run multi-step workflows after OCR, such as classify document, extract fields, check rules, update CRM/ERP, and notify a user.
Often stops at text extraction or file conversion, requiring Zapier/RPA/scripts for validation and follow-up actions.
Cross-channel data consistency
Keeps extracted text, metadata, prompts, decisions, and outputs in one shared workflow record.
Data can be split across OCR exports, spreadsheets, storage folders, tickets, and automation logs.
Cost and usage scaling
Consolidates OCR plus automation tasks into one subscription, reducing duplicate seats and overlapping API fees.
Free tiers are useful for low volume, but higher page counts, API calls, exports, and automation steps often create separate charges.
Exemplos de fluxos de trabalho
Prompts reais que você pode copiar para o agente acima.
Free AI OCR: Extract Structured Data from Invoices as JSON Array
You are a Free AI OCR assistant. I will provide one or more invoice, receipt, or financial document images/PDFs. Your task is to use OCR to extract all readable text and convert the key information into a structured JSON array. Requirements: 1) Detect vendor name, invoice/receipt number, date, due date if available, currency, subtotal, tax, total, payment status, line items, and any notes. 2) Preserve original text where possible. 3) If a field is missing or unreadable, use null. 4) Include a confidence score from 0 to 1 for each document. 5) Return only a valid JSON array, with no extra commentary. JSON schema to follow: [{"document_name":"string","document_type":"invoice|receipt|financial_record|unknown","vendor_name":"string|null","document_number":"string|null","date":"YYYY-MM-DD|null","due_date":"YYYY-MM-DD|null","currency":"string|null","subtotal":number|null,"tax":number|null,"total":number|null,"payment_status":"paid|unpaid|partial|unknown","line_items":[{"description":"string","quantity":number|null,"unit_price":number|null,"amount":number|null}],"extracted_raw_text":"string","unreadable_sections":["string"],"confidence":number}]. Process these files: [UPLOAD OR LINK DOCUMENTS HERE].
[{"document_name":"invoice_march_2025.pdf","document_type":"invoice","vendor_name":"Acme Office Supplies","document_number":"INV-20491","date":"2025-03-14","due_date":"2025-04-13","currency":"USD","subtotal":245.00,"tax":19.60,"total":264.60,"payment_status":"unpaid","line_items":[{"description":"Printer paper - 10 reams","quantity":2,"unit_price":45.00,"amount":90.00},{"description":"Ink cartridge black","quantity":3,"unit_price":35.00,"amount":105.00},{"description":"Desk organizer","quantity":1,"unit_price":50.00,"amount":50.00}],"extracted_raw_text":"Acme Office Supplies Invoice INV-20491 Date 03/14/2025 Due 04/13/2025...","unreadable_sections":[],"confidence":0.94}]
Free AI OCR: Convert Scanned PDFs into Searchable Text JSON Array
You are a Free AI OCR assistant specialized in scanned PDFs and image-based documents. I will provide scanned PDF pages or document images. Your job is to extract the full readable text and return it as a clean JSON array suitable for search, indexing, or downstream automation. Requirements: 1) Process each page separately. 2) Keep paragraph order and headings where possible. 3) Identify language per page. 4) Mark tables, signatures, stamps, handwritten sections, and low-confidence areas. 5) Do not summarize unless I explicitly ask. 6) Return only valid JSON array output. JSON schema to follow: [{"file_name":"string","page_number":number,"detected_language":"string|null","full_text":"string","sections":[{"section_type":"heading|paragraph|table|footer|header|signature|stamp|handwriting|unknown","text":"string","confidence":number}],"tables":[{"table_index":number,"headers":["string"],"rows":[["string"]]}],"warnings":["low_resolution|blurred_text|skewed_page|partial_page|unknown_language|none"],"confidence":number}]. Process these files: [UPLOAD OR LINK SCANNED PDF/IMAGES HERE].
[{"file_name":"scanned_contract.pdf","page_number":1,"detected_language":"English","full_text":"Service Agreement This agreement is entered into between...","sections":[{"section_type":"heading","text":"Service Agreement","confidence":0.98},{"section_type":"paragraph","text":"This agreement is entered into between the parties listed below.","confidence":0.95},{"section_type":"signature","text":"Signature area detected; signature not transcribed.","confidence":0.82}],"tables":[],"warnings":["none"],"confidence":0.93},{"file_name":"scanned_contract.pdf","page_number":2,"detected_language":"English","full_text":"Payment Terms The client agrees to pay...","sections":[{"section_type":"heading","text":"Payment Terms","confidence":0.97},{"section_type":"paragraph","text":"The client agrees to pay all invoices within 30 days.","confidence":0.96}],"tables":[{"table_index":1,"headers":["Milestone","Amount","Due Date"],"rows":[["Deposit","$1,000","Upon signing"],["Final Payment","$2,500","Project completion"]]}],"warnings":["skewed_page"],"confidence":0.89}]
Free AI OCR: Transcribe Handwritten Forms and Notes into Clean JSON Array
You are a Free AI OCR assistant for handwriting and form digitization. I will provide images or PDFs of handwritten notes, classroom notes, intake forms, survey forms, or field forms. Extract the readable content and convert it into a structured JSON array. Requirements: 1) Separate printed text from handwritten text. 2) Capture form labels and their corresponding answers. 3) Preserve uncertain words using [?] after the word. 4) If handwriting is unreadable, use null and explain in an uncertainty field. 5) Normalize dates and phone numbers when possible. 6) Return only a valid JSON array. JSON schema to follow: [{"document_name":"string","document_type":"handwritten_note|form|survey|intake_form|unknown","detected_language":"string|null","printed_text":"string","handwritten_text":"string","fields":[{"label":"string","value":"string|null","source":"printed|handwritten|mixed","confidence":number}],"normalized_entities":{"names":["string"],"dates":["YYYY-MM-DD"],"phone_numbers":["string"],"emails":["string"],"addresses":["string"]},"uncertainties":[{"text_or_field":"string","reason":"unreadable|ambiguous|cut_off|low_quality|unknown","suggested_review":"string"}],"confidence":number}]. Process these files: [UPLOAD OR LINK HANDWRITTEN DOCUMENTS HERE].
[{"document_name":"student_notes_photo.jpg","document_type":"handwritten_note","detected_language":"English","printed_text":"","handwritten_text":"Biology notes: Photosynthesis converts light energy into chemical energy. Chlorophyll absorbs sunlight. Equation: 6CO2 + 6H2O -> C6H12O6 + 6O2.","fields":[],"normalized_entities":{"names":[],"dates":[],"phone_numbers":[],"emails":[],"addresses":[]},"uncertainties":[{"text_or_field":"word after Chlorophyll","reason":"ambiguous","suggested_review":"Verify whether the word is 'absorbs' or 'captures'."}],"confidence":0.87},{"document_name":"patient_intake_form.png","document_type":"intake_form","detected_language":"English","printed_text":"Name Date Phone Email Symptoms","handwritten_text":"Jane Smith 2025-05-02 555-0198 jane@example.com headache and nausea","fields":[{"label":"Name","value":"Jane Smith","source":"handwritten","confidence":0.96},{"label":"Date","value":"2025-05-02","source":"handwritten","confidence":0.94},{"label":"Phone","value":"555-0198","source":"handwritten","confidence":0.91},{"label":"Email","value":"jane@example.com","source":"handwritten","confidence":0.93},{"label":"Symptoms","value":"headache and nausea","source":"handwritten","confidence":0.88}],"normalized_entities":{"names":["Jane Smith"],"dates":["2025-05-02"],"phone_numbers":["555-0198"],"emails":["jane@example.com"],"addresses":[]},"uncertainties":[],"confidence":0.92}]
Referência
Outras ferramentas nesta área
Soluções isoladas que cobrem partes deste fluxo. O agente acima resolve todas elas em uma única conversa.