Revolucionarna AI sistema za digitalizacijo in klasifikacijo zgodovinskih dokumentov

Sistem uporablja napredno neuronalno mrežo, ki je posebeje izobražena za opazovanje zgodovinskih napisov in jezikov. Može procesirati različne vrste napisov, vključno gotiški, humanistični in novo-gotiška. Vsebina sistema vsebuje širjo zbirjo zgodovinskih fontov in napisnih stilov, ki se kontinuoje. Za vsak dokumentni tip se sistem automatsko izbere najbolj ugodno OCR model. Može delovati s več kot 20 zgodovinskimi jeziki, vključno latinščino, staroceskoe, nemščino in grščino. V primeru nepoznanih napisnih tipov se lahko sistem dodatno treningira na novih vzorcih.

OCR accuracy za poškane dokumente zavisuje po meri in vrsti škod, vendar sistem doseže srednjo uspešnost med 85-95% tudi pri problematičnih materialih. Uporablja kombinacijo več OCR motorov in napredno sestavljeno obravnajo za obdelavo slike, ki vključuje adaptivno binarizacijo, odstranjevanje šuma in obnovo zanesenih delov. Sistem lahko compensira za opuščene tekstove, prahove, zvozde in druge obširne vrste škod. Za severno poškano dokumente ponuja opcijo semi-automatske obravnje s človeškim nadzorom.

Avtomatska klasifikacija dokumentov se izvaja v več fazah. Prvo, sistem analizira vizualne lastnosti dokumenta (nastava, tip fonta, grafische elemente). Potem se izvede sadrževna analiza z uporabo NLP (prirodoslovno jezikovna procesiranja) za identifikacijo ključnih tem, datumov in entitet. Na podlagi tega informacije se dokument categorizira v preddefinirane kategorije. Sistem uporablja hierarhisko klasifikacijsko model, ki omogoča multi-levelnje sortiranja po različnih kritere (obdobje izvora, dokumentni tip, tematika, jezik in dr.).

Basic hardware requirements include high-performance scanners with high resolution (at least 300 DPI) and specialized lighting for historical documents. Processing requires a server with a powerful GPU for AI computations (at least NVIDIA RTX 3080 or equivalent) and sufficient RAM (minimum 32 GB). Storage must be sized for the expected data volume with redundancy. Using SSDs for active data and tape libraries for archiving is recommended. The network infrastructure should support fast transfer of large data volumes.

The security of digitized documents is ensured by a multi-level protection system. All data is encrypted both during transmission and storage (AES-256). The system uses redundant storage with automatic backup in multiple locations. Access to documents is controlled by roles with multi-factor authentication. Automatic data integrity checks and checksum creation are performed regularly. For critical documents, it is possible to set special security policies, including logging of all accesses and changes.

The system supports a wide range of output formats suitable for various purposes. For archiving, the lossless high-resolution TIFF format is used. For common use, documents are available in PDF/A (archival standard), JPEG2000, and PNG formats. The text layer is stored in Unicode with XML/TEI support for structured documents. Metadata is exported in standardized formats such as METS, MODS, and Dublin Core. The system also allows generating previews in various resolutions and optimized versions for web browsing.

The staff onboarding process is divided into several phases and typically takes 2-3 weeks. Basic operation of the digitization and cataloging system can be mastered in 2-3 days of intensive training. Advanced features like classification scheme management and OCR optimization require an additional week of training. An extended two-week course is intended for system administrators. Training includes hands-on practice with real documents and solving typical problematic situations. The basic training is followed by a period of supervised work.

The system offers flexible integration options with commonly used archival and library systems. It supports standard protocols for data exchange (OAI-PMH, Z39.50, SRU/SRW) and common API interfaces (REST, SOAP). Metadata can be synchronized with existing catalogs and digital libraries. The system allows mapping of custom classification schemes to standard formats and taxonomies. For specific requirements, custom connectors and integration bridges can be developed.

Multilingual processing and historical spelling variations are handled using specialized language models and dictionaries. The system contains an extensive database of historical word variants and spelling forms for various languages and periods. It utilizes contextual analysis for correct interpretation of historical texts. For each document, the primary language and period can be specified, increasing the recognition accuracy. The system also supports automatic language detection and transcription into modern orthography.

The system provides comprehensive tools for post-processing digitized documents. It includes an editor for manual OCR text corrections with a visual comparison of the original and recognized text. It allows batch edits and the application of rules to fix common errors. It supports a versioning system that keeps track of all changes. For collaboration among multiple proofreaders, a workflow system is available with the ability to assign tasks and monitor work progress. Corrected texts can be automatically propagated to all output formats.