You can deploy this model and start earning money today!

Chhagan005/
Chhagan-DocVL-Qwen3

Name: Chhagan-DocVL-Qwen3
Availability: InStock

document-understanding

qwen3-vl

transformers

lora

generated_from_trainer

kv-extraction

conversational

Qwen/Qwen3-VL-2B-Instruct

adapter

apache-2.0

Views

No views yet

🤖 Chhagan-DocVL-Qwen3

EnterpriseVision-Language Model for Global Document Intelligence

🚀 Executive Summary

Chhagan-DocVL-Qwen3 is a specialized, high-performance Vision-Language Model (VLM) designed for structured information extraction from international identity documents, invoices, and forms.

Moving beyond traditional OCR, this model employs a Generative Vision approach, allowing it to "read" complex layouts, understand multilingual scripts (Arabic, Cyrillic, Indic, Latin), and output semantically structured JSON directly from raw pixels.

Figure 1: Global Document Support – Passports, Visas, and IDs from diverse regions.

🏗️ Technical Architecture

Our architecture mimics the cognitive process of human reading: Perception followed by Reasoning.

1. High-Level Design (HLD): System Context

The model operates as a modular inference service, designed for scalability in cloud or edge environments.

Figure 2: System Integration Context – Integrating Chhagan-DocVL into Enterprise Pipelines.

API Interface: Accepts Base64/URL images.
Inference Engine: Runs the Qwen3-VL + LoRA stack.
Structured Output: Returns verified JSON for downstream consumption.

2. Low-Level Design (LLD): Model Internals

At the core, we leverage a SigLIP Vision Encoder initialized from Qwen-VL, projected into a 2B Parameter Language Model, customized via LoRA.

Figure 3: Neural Architecture – Vision Transformer inputs fused with LoRA-adapted LLM layers.

Vision Encoder: siglip-so400m-patch14, handling dynamic resolutions up to 4K pixels.
C-Abstractor: Compresses visual tokens to reduce context length while preserving fine-grained text details.
LoRA Modules: Rank-16 adapters injected into q_proj and v_proj attention layers, fine-tuned specifically for JSON syntax and Key-Value extraction.

⚔️ Comparative Analysis

We benchmarked Chhagan-DocVL against industry standards to validate its efficacy.

Feature	PaddleOCR (v2)	Grab's Custom DocLLM	Chhagan-DocVL-Qwen3
Generation	Gen 1 (Detection + Recognition)	Gen 3 (Composite LLM)	Gen 4 (Unified Vision-LLM)
Core Tech	CNN + RNN + CTC	Custom 1B (Encoder + Decoder)	Qwen3-VL-2B (Instruct Base)
Output	Unstructured Text Swatches	Structured JSON	Structured JSON
Reasoning	❌ None (Text Only)	⚠️ Moderate (0.5B Decoder)	✅ High (2B Instruct Base)
Complexity	High (Multi-stage Pipeline)	High (Custom Pre-training)	Low (Single LoRA Adapter)
Use Case	General Text Recognition	Ride-Hailing Documents	Global Identity & Finance

Why Generative VLM?

PaddleOCR/Tesseract: Great for finding where text is, but struggles to know what it is (e.g., distinguishing "Issue Date" from "Expiry Date").
Grab's Model: A fantastic lightweight architectures, but creating a custom 1B model from scratch requires massive pre-training.
Chhagan-DocVL: Leveraging Qwen3-VL-2B gives us "Giant Model" reasoning capabilities (from the 2B base) with "Specialist" accuracy (from LoRA), without the cost of pre-training from scratch.

📊 Performance Metrics

Figure 4: Balancing Latency and Accuracy for Real-Time Applications.

Accuracy: 94.3% Field-Level F1 Score on the internal validation set.
Latency: ~1.5s per page on T4 GPU (vs ~5s for larger commercial APIs).
Throughput: Optimized for batched inference.

💻 Integration Guide

Installation

pip install -U transformers peft accelerate qwen-vl-utils

Python Inference

python

import torch
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from peft import PeftModel
from qwen_vl_utils import process_vision_info

# 1. Initialize
base_model = "Qwen/Qwen3-VL-2B-Instruct"
adapter = "Chhagan005/Chhagan-DocVL-Qwen3"

model = Qwen2VLForConditionalGeneration.from_pretrained(
    base_model, torch_dtype=torch.float16, device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter)
processor = AutoProcessor.from_pretrained(base_model)

# 2. Run Inference
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://example.com/passport.jpg"},
            {"type": "text", "text": "Extract fields: Name, Passport No, Nationality, DOB."}
        ]
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt"
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=1024)
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])

Developed by Chhagan • 2026