Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- vision
+- document-ai
+- donut
+- ocr-free
+- image-to-text
+---
+
+# document_parsing_donut_v1
+
+## Overview
+This model is an implementation of the **Donut** (Document Understanding Transformer) architecture. Unlike traditional OCR-based systems, this model is OCR-free, meaning it maps raw document images directly to structured JSON outputs. It is fine-tuned to parse complex layouts such as invoices, receipts, and technical forms without a separate text recognition step.
+
+
+
+## Model Architecture
+The model utilizes a vision-encoder text-decoder framework:
+- **Encoder**: A Swin Transformer that processes high-resolution images into visual features.
+- **Decoder**: A BART-based multi-lingual transformer that generates text tokens in a sequence-to-sequence manner.
+- **Objective**: The model is trained using a cross-entropy loss to predict the next token based on both the visual input and preceding tokens:
+$$\mathcal{L} = -\sum_{t=1}^{T} \log P(y_t | y_{<t}, \mathbf{x})$$
+
+## Intended Use
+- **Automated Data Entry**: Extracting key-value pairs from digitized business documents.
+- **Layout Analysis**: Identifying structural components (headers, tables, footers) in multi-page PDFs.
+- **Archival Digitization**: Converting historical scanned documents into searchable, structured data.
+
+## Limitations
+- **Resolution Sensitivity**: Performance drops significantly if images are scaled below 960x1280 pixels.
+- **Language Bias**: While capable, accuracy is highest for Latin-script documents; CJK and Arabic scripts require specialized fine-tuning.
+- **Handwriting**: The model is optimized for printed text and may struggle with highly cursive or disorganized handwriting.