EZ Parser

EZ Parser provides rule-driven extraction of fields from freeform text and documents. Conceptually, it treats input_text as a stream to be segmented into structured records using either regex capture groups or template-based patterns.
Users define extraction_rules that map field names to patterns, types, and post-processing steps. The tool then runs a deterministic parse, validates field types, and outputs a structured document per input.
This enables reliable data ingestion, content mining, and automated QA checks. Core value lies in replacing manual copy-paste parsing with repeatable templates that adapt to similar data sources, such as invoices, logs, forms, or article metadata.
It supports batch processing for multiple documents, and can export outputs as JSON, CSV, or YAML for downstream pipelines. Unique differentiators include template-based parsing alongside robust regex support, field-level normalization, and optional confidence scoring to indicate match reliability.
Typical use cases include extracting line items from invoices, metadata from articles, or key-value pairs from logs, with the ability to define validation rules and defaults. This tool reduces time-to-insight and improves data consistency across teams while minimizing errors introduced by manual parsing.