قراءة المستندات العربية حقلًا حقلًا

المشكلة

Organisations processing Arabic documents needed reusable capabilities for entity extraction, summarisation, classification, search, and structured information retrieval. In practice every Arabic document project rebuilt the same foundations - normalisation, OCR handling, label sets, evaluation - before reaching anything specific to the customer. The cost of finding out whether a use case was viable therefore approached the cost of delivering it, which is what kept most of these projects from starting.

ما بنيناه

We built an accelerator combining Arabic named-entity recognition, document classification, summarisation, OCR-ready preprocessing, semantic embeddings, and configurable output schemas, with notebook experiments and packaged components for rapid evaluation against customer samples before production integration.

ما توقّف عليه

Coverage rose because OCR, entity recognition, page zones, and validation rules were combined rather than expecting one model to recover every value - and page-level provenance plus review routing kept higher coverage from meaning that more weak predictions were silently accepted as authoritative business data.