docTR vs Open-Capture

docTR is a free deep-learning OCR library for finding and recognizing text in documents, aimed at document automation and research. Its pretrained OCR predictors first detect text and then recognize it, while a layout predictor identifies regions such as tables, figures, and headers. It reads PDFs and images; web-page input requires the optional HTML extra. Results can be rendered as plain text or exported as a JSON-serializable nested dictionary. The project describes its predictors as optimized for CPU and GPU inference. Users can train custom detection, recognition, layout, and table-structure models when pretrained models are not suitable. Documentation covers loading and sharing models through the Hugging Face Hub, and includes a minimal REST API deployment template and a browser demo. Installation is available through pip, Git, or official Docker images. The library requires Python 3.11 or higher, and pretrained weights download on first use and are cached locally. For AWS Lambda, the guide advises disabling multiprocessing and placing the model cache within /tmp. The project uses the Apache License 2.0.

Open-Capture collects documents, extracts information with OCR and AI, and exports results to third-party management software. It accepts material from scanners, copiers, file drops, email and attachments, web forms, uploads, and web services. Extraction options include Tesseract OCR, pre-trained models, machine-learning classification, key-value extraction, reference-data matching, dictionaries, and barcode or QR-code reading. Extracted fields can be checked against rules, formats, reference data, and scripted business rules. The full-web application runs natively on Linux and can be hosted on-premise or in a public or private cloud. It is free and open-source under the GNU GPLv3 license, with no limit on users. Optional modules include Splitter for preparing batches, Verifier for checking extracted data, and MailCollect for email processing. MailCollect analyzes messages, identifies senders, extracts metadata, and routes attachments into processing workflows. Verifier is described for structured or semi-structured documents such as invoices, delivery notes, contracts, and purchase orders. Listed connections include a native MEM Courrier connector, CMIS, OpenADS/Oxalis, OpenCRM, Alfresco, and SharePoint; export formats include PDF, PDF/A, XML, CSV, and plain text.