OCR It is an unpacked Chrome extension that captures a fixed on-screen region from paginated documents, recognizes its text locally, and builds an editable transcript. Users draw the capture region once, then press Option-Shift-S on each page; screenshots are queued while bundled Tesseract OCR runs in the background. Option-Shift-A can automate capture and page turning until the document ends, while Option-Shift-R redraws the region. Exports preserve page order with page-number separators, and each captured page retains a thumbnail for checking crop alignment or rerunning faulty OCR. The extension makes no outbound network requests and requires no API key. It ships with English, Portuguese, and Spanish Tesseract models; additional models from Tesseract’s roughly 100 supported languages can be bundled before installation. Automatic page turning can target a clicked screen coordinate or dispatch a key such as ArrowRight, including in cross-origin iframes and Shadow DOM when the necessary site permission is granted. Runs stop after two identical pages by default, on OCR failures, failed page turns, a 300-page cap, tab closure, or browser restart. Chrome’s PDF viewer supports manual capture and text extraction, but cannot be auto-advanced because extensions cannot inject into its plugin. OCR accuracy depends on source quality; crisp rendered text is reported to reach more than 90% confidence, while scans and handwriting may need cleanup.

OCR 4.1 is a new OCR service from Mistral AI that includes native paragraph-level bounding box extraction, structural block labels, and block-level confidence scores. Pricing is set at €3.5 per 1000 pages and €4.38 per 1000 annotated pages.

Mistral OCR 4 features bounding boxes, block classification, and inline confidence scores with extracted text. It supports 170 languages, operates in a single container for self-hosted deployments, and serves as an ingestion component for enterprise search and retrieval pipelines.

Interfaze is a new model architecture that surpasses Gemini-3-Flash, Claude-Sonnet-4.6, GPT-5.4-Mini, and Grok-4.3 in accuracy across nine benchmarks in OCR, vision, speech-to-text, and structured output tasks. The model addresses inefficiencies in human performance on complex computer-level tasks, enhancing capabilities in mapping and translation.