Overview

MayankLad31/invoice_schema is a specialized 1.5 billion parameter model, fine-tuned using Reinforcement Learning (GRPO on Qwen2.5-Coder), engineered for extracting structured JSON data from OCR (Optical Character Recognition) text. Its core capability lies in processing document text, such as invoices, and converting it into a user-defined JSON format.

Key Capabilities

• Schema-driven JSON Extraction: Users can provide any JSON schema, and the model will attempt to extract corresponding data from the input text.
• OCR Integration: Designed to work in conjunction with OCR tools (like PaddleOCR) to process scanned documents or images.
• Local Deployment: The model is available in a GGUF format, enabling 100% local execution for privacy-sensitive or offline applications.
• Invoice Processing: Particularly effective for extracting details like invoice numbers, recipient information, payment details, itemized lists, and totals from invoice documents.

Good for

• Automating data entry from scanned invoices or receipts.
• Developing custom document processing pipelines where data structure is critical.
• Applications requiring local, offline data extraction capabilities.
• Developers looking for a specialized model to convert unstructured OCR text into structured JSON based on a flexible schema.