We use Tesseract, Google Document AI, Azure Form Recognizer, and custom deep learning models (CNNs, transformers) for handwritten and printed text. We choose based on accuracy, language support, and layout complexity.

Yes. We use ICR (Intelligent Character Recognition) and custom models for handwriting. For degraded or scanned documents, we apply preprocessing (deskew, denoise, binarization) and train on domain-specific data to improve accuracy.

We use layout analysis to detect tables, key-value pairs, and fields. We combine OCR with NER and validation rules. We output JSON, databases, or ERP formats. We handle template-based and variable-layout documents with human-in-the-loop for edge cases.

Yes. We support 100+ languages with Tesseract and cloud APIs. We handle mixed-language documents and script detection. We fine-tune models for low-resource languages and domain-specific terminology.

We use confidence scores, validation rules (dates, amounts, IDs), and human review workflows. We provide correction UIs and feedback loops to improve models. We report accuracy metrics (character, word, field-level) per document type.

Yes. We deploy on AWS, Azure, GCP, or on-premise. We use Docker/Kubernetes for scale. We support batch processing (queues) and real-time APIs. We handle secure storage and retention per your compliance needs.

Standard projects take 6–10 weeks: requirement analysis, model selection/training, integration, and deployment. Complex multi-document or custom model projects may take 3–6 months. We provide MVP and phased rollouts.