This article examines the methodological shift in ancient-text studies within the digital humanities, with particular attention to cuneiform evidence. Rather than treating computational methods as replacements for traditional philology, it considers how digitization, transcription, normalization, annotation, and representation reshape the evidence itself. Using the distinction between “Data” and “Capta,” the study adopts a critical narrative and task-oriented review of selected scholarship and assesses computational approaches in relation to analytical tasks, data limitations, evaluation procedures, and historical interpretation. The article distinguishes three connected methodological regimes: philological analysis, digital corpora, and machine learning and artificial intelligence. It then proposes a five-layer framework linking image, sign, sequence, structure, and conceptual model. The framework is intended as a methodological synthesis rather than a new algorithm and organizes computer vision, natural language processing, statistical and network analysis, and multimodal methods according to the evidence handled at each stage. Because ancient textual corpora are often fragmentary, sparse, imbalanced, and uncertain, computational outputs should be treated as probabilistic rather than definitive. Errors introduced during image processing, sign recognition, transcription, or structural analysis may affect later linguistic and historical interpretations. Evaluation should therefore combine task-specific metrics, component-level and end-to-end testing, error analysis, and expert assessment. Historical interpretation ultimately depends on transparent provenance, philological judgment, archaeological context, and continued human involvement throughout the analytical workflow..