OCR Development Services

Automated document capture from every source your business uses: email attachments ingested via IMAP or Microsoft Graph API (monitoring specific mailboxes for attachments matching defined criteria), upload portals where vendors or staff submit documents directly, network folder polling for batch drops, and REST API submission for system-to-system document handoff. Multi-format support covering digital PDFs, scanned PDFs, JPEG and PNG images from mobile captures, TIFF files from legacy scanner systems, and multi-page documents that need splitting before individual page processing. Deduplication by file hash or document identifier prevents the same invoice from being processed twice when it arrives via two channels. Processing status tracking gives your operations team visibility into the current queue length and per-document processing state.

Image quality preprocessing that addresses the real-world scan conditions that break naive OCR: deskewing documents photographed at up to 15-degree angles (common with phone-captured invoices), contrast normalization for faded thermal receipts and photocopies, noise removal for fax transmission artifacts, and resolution upscaling for images below the OCR-reliable 300 DPI threshold. Automatic orientation detection and correction handles documents scanned upside-down or rotated 90 degrees. Multi-page PDF splitting separates cover pages, appendices, and distinct document types within a single file before individual page processing. Page classification assigns a document type to each page when a single scan contains multiple document types (invoice front + PO attachment). These preprocessing steps are the difference between 70% OCR accuracy on real-world documents and 95%+.

The preprocessing pipeline is built on OpenCV for image manipulation operations: adaptive thresholding (Otsu's binarization) to separate foreground text from variable-brightness backgrounds, morphological operations to close gaps in broken characters, and Hough transform-based skew correction that measures the dominant line angle from detected horizontal rules or text baselines. For documents with severe perspective distortion (phone photos of flat documents), a four-point perspective transform corrects the trapezoid artifact before OCR runs. Tesseract 4.x with LSTM neural network mode processes the cleaned image; for higher-value documents or handwritten fields, AWS Textract or Google Document AI is called instead, the engine selection is made per document type based on accuracy benchmarks run during the scoping phase. Layout analysis using Detectron2 LayoutParser or PaddleOCR's layout module identifies text regions, table regions, and figure regions before extraction, so table cells are not conflated with paragraph text and empty regions don't generate phantom extractions.

Extraction of the specific data fields your downstream system needs, invoice header fields (vendor name, invoice number, date, PO reference, payment terms), line items (description, quantity, unit price, tax, line total), totals (subtotal, tax amount, grand total), and custom fields specific to your document types. Template-based extraction for vendors who use consistent formats (matching known layouts for fast, high-accuracy extraction). AI-powered layout-aware extraction (Azure Document Intelligence, Google Document AI, or custom LayoutLM models) for variable-format documents from suppliers who change their invoice layout or send different formats for different order types. Table extraction using grid detection algorithms for line item tables that span multiple columns and rows. Confidence scoring for every extracted field so you know which values to trust and which to route for review.

Field-level validation before any extracted data reaches your system: format validation (invoice numbers matching your expected pattern, dates in valid ranges, amounts within plausible bounds), required field presence (any invoice missing a PO number routes to review rather than being processed with a blank field), and cross-field consistency (line item totals summing to the subtotal, tax calculated correctly against the applicable rate). Business rule validation against your reference data: vendor codes and supplier IDs looked up against your approved vendor list, PO numbers validated against open purchase orders in your ERP, and currency codes checked against your accepted currencies. Discrepancies above a configurable tolerance threshold (e.g., ±1% for rounding on international invoices) surface for review rather than silently creating mismatches between the extracted values and expected amounts.

Regex patterns enforce the structural format of each field type: invoice numbers typically follow a vendor-specific pattern (e.g., INV-[0-9]6 or [A-Z]2[0-9]8), dates are normalised from ambiguous regional formats (01/02/2025 parsed correctly as DD/MM or MM/DD based on vendor locale), and amounts are cleaned of currency symbols and thousand-separator commas before numeric validation. Confidence score thresholds are set per field based on the cost of a missed error: a misread invoice total routes to human review at confidence below 0.92, while a secondary address field might be accepted at 0.75. Cross-field validation catches the extraction errors that confidence scores miss: a line item unit price of $0.05 against a grand total of $5,000 signals either a quantity error or extraction failure and routes to review regardless of individual field confidence. Documents that fail validation are never silently discarded, they enter the human review queue with the specific validation failure reason displayed alongside the document so reviewers can focus on the problem field rather than re-reading the entire document.

Web interface where your operators review documents that didn't pass straight-through processing, built for efficiency in high-volume review queues, not as an afterthought. Original document displayed on the left with each extracted field highlighted in its source location; extracted values with confidence scores displayed on the right, with low-confidence fields highlighted in amber. One-click accept or inline correction for each field, with keyboard shortcuts for the common actions reviewers perform repeatedly. Batch review mode presents multiple similar exceptions in a unified interface so reviewers can process 40-50 documents per hour rather than opening each individually. Corrections are logged with the reviewer's ID and timestamp for audit purposes, and fed back into the extraction model's retraining pipeline so the system learns from each correction and the exception rate decreases over time.

Structured output delivered to your downstream system in the format it consumes: JSON via REST API webhook for real-time downstream processing, parameterized SQL insert/upsert for direct database writes, IDoc or BAPI calls for SAP integration, REST API calls to NetSuite or Dynamics, or XML for older ERP systems with file-based interfaces. Output schema maps extracted field names to your target data model exactly, the PO number field in the document maps to the purchaseOrderId column in your database, not a generic field name that requires downstream transformation. Delivery triggered by processing completion (for real-time workflows) or on a configurable schedule for batch processing windows. Full processing audit trail records every document: receipt timestamp, processing steps completed, fields extracted with confidence scores, validation results, any human corrections made, and output delivery confirmation with timestamp.