Enterprise-level OCR + Small Language Model Selection Practice: A Complete Methodology from Model Evaluation to MVP Implementation

This article shares the 8-week enterprise AI service project practice of South Korea's Uncommon Lab. Addressing the dilemma of OCR and SLLM selection in enterprise document intelligent processing, it selects the optimal combination of PaddleOCR, Gemma, Qwen, etc., through systematic model evaluation, builds a production-level document processing service prototype based on FastAPI, and provides a complete methodology from model evaluation to MVP implementation.

In the enterprise service field, intelligent document processing (contract review, invoice recognition, report analysis) relies on two core technologies: OCR and LLM. However, with numerous open-source models available, enterprises struggle to balance accuracy, speed, and cost. South Korea's Uncommon Lab launched an 8-week project, aiming to select an OCR+SLLM tech stack suitable for business scenarios through a systematic evaluation process and quickly build a deployable MVP.

The project established a multi-dimensional evaluation system oriented to actual business:

1. Language recognition accuracy: Emphasizes bilingual capabilities in Korean and English (to handle mixed-language documents);
2. Layout recognition ability: Restores complex layouts such as tables and columns;
3. Processing speed: Measured by "per-page inference latency" (to adapt to batch processing scenarios);
4. Document type adaptability, system stability (failure rate), and cloud deployment cost.

Candidate model research:

• OCR: PaddleOCR (open-source by Baidu, well-supported for Chinese, active community);
• SLLM: Google Gemma series, Alibaba Qwen series (lightweight design, multi-language capabilities, focusing on efficient execution of vertical tasks, reducing resource consumption and latency).

The core of evaluation is "let real data speak":

1. Collect various business documents such as contracts and receipts as test sets (more in line with reality than public benchmarks);
2. Each model undergoes deployment verification and standardized scoring, with results presented in structured reports (quantitative + qualitative);
3. A rigorous process avoids "arbitrary" selection and reduces the risk of later rework.

After determining the optimal combination, FastAPI is used to build the backend service with a pipeline design: document input → OCR text extraction → SLLM intelligent analysis → structured output. The code repository structure is standardized: data (test samples), docs (project documents), results (evaluation results), scripts (test scripts), src (core service code). The modular design facilitates expansion and maintenance.

Industry insights from this project's methodology:

1. Model selection should be based on actual business data, not public rankings (document characteristics vary greatly across industries);
2. Evaluation dimensions should be comprehensive (accuracy + latency + cost + stability);
3. Rapid prototype verification reduces risk (8 weeks of focused investment to verify the feasibility of the technical route).

The development of the open-source AI ecosystem gives enterprises more choices but also increases the difficulty. This project demonstrates a systematic selection methodology: clarify requirements → design evaluation dimensions → collect real data → perform comparative tests → rapid prototype verification, providing a reference practical path for the implementation of enterprise document intelligence.