MayankLad31/invoice_schema
MayankLad31/invoice_schema is a 1.5 billion parameter model, fine-tuned with Reinforcement Learning (GRPO on Qwen2.5-Coder), designed for extracting structured JSON data from OCR text based on user-defined schemas. This model specializes in invoice processing, allowing users to define a schema and extract relevant information from scanned documents. Its primary use case is local, schema-driven data extraction from OCR output, particularly for invoices.
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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.