Neuro-Linguistic Cognitive System for Arabic Manuscript Analysis

Neuro-Linguistic Cognitive System for Arabic Manuscript AnalysisThis study presents a comprehensive Neuro-Linguistic Cognitive System designed to transform the processing, analysis, and epistemological interpretation of Arabic manuscripts through an integrated, multi-layered hybrid architecture that combines artificial intelligence, computer vision, cognitive linguistics, and manuscript-specific paleographic modeling. Unlike conventional digital philology pipelines that limit themselves to surface digitization or OCR-based transcription, the proposed framework shifts the focus toward reconstructing the latent cognitive and linguistic architectures embedded within historical manuscripts. The system incorporates OpenCV-based visual preprocessing, Tesseract/EasyOCR optical decoding, Farasa and Camel Tools morphological–syntactic processing, NLTK semantic modeling, and JSON-LD knowledge-graph construction. These modules work together to treat the manuscript as a dynamic epistemic entity that encodes conceptual, linguistic, and cognitive structures reflective of its historical milieu. Experimental testing on 10th–13th century Arabic manuscripts demonstrates significant improvements over leading platforms such as Transkribus, Kraken, and eScriptorium: OCR accuracy improved by 28%, semantic precision by 35%, and overall cognitive-inference stability by approximately 30%. Statement of NoveltyThis preprint introduces the first interdisciplinary cognitive–computational system dedicated specifically to Arabic manuscript analysis. It unifies neural cognition, linguistic modeling, paleographic variation encoding, and probabilistic reconstruction algorithms into a single integrated architecture. This approach establishes a new analytical paradigm—Neuro-Traditional Linguistics—bridging classical Arabic philology with modern AI-driven manuscript technologies, and providing unprecedented accuracy in reconstruction, classification, and cognitive modeling compared to OCR-only or rule-based systems. Methodology OverviewThe system follows a hierarchical processing pipeline consisting of: Visual preprocessing (denoising, ink–background separation, structural contour extraction) Optical decoding through hybrid Tesseract/EasyOCR modules Morphological and syntactic parsing via Farasa and Camel Tools Semantic mapping using NLTK-based models Paleographic feature encoding to capture stroke variability, letterform evolution, and scribal idiosyncrasies Cognitive reconstruction algorithms simulating manuscript-era linguistic and epistemic reasoning This multi-level design allows the system to detect linguistic patterns, classify stylistic structures, reconstruct damaged text segments, and generate predictive models simulating medieval scribal cognition. KeywordsArabic Manuscripts; Cognitive Linguistics; Computational Linguistics; Digital Philology; Neural Systems; Hybrid AI Models; Manuscript Analysis; Paleography; Text Reconstruction; Knowledge Graphs