How to Extract Financial Statement Data with Sparrow & Vision LLM

Extract financial statement data with Sparrow and Vision LLM in this quick tutorial! Sparrow auto-detects tables, builds clear grids, and uses OCR for accurate Vision LLM results, preventing errors. Runs locally with no cloud dependency, making it great for private financial documents. Perfect for anyone handling sensitive financial data. 

 

Boost Vision LLM Accuracy with OCR Text Integration

I show an interesting approach where I send both an image and OCR text to a Vision LLM. The prompt is constructed to instruct the Vision LLM to prioritize the OCR text. This allows the use of a Vision LLM for structured output construction while relying on external OCR text, giving you more control over the results.

 

Solving Vision LLM Number Formatting Issues Using PaddleOCR and Sparrow

Discover how to fix number formatting errors in vision LLMs like Mistral! In this video, I show how Mistral misreads "56,000" as "56000" and how combining PaddleOCR’s text extraction with Sparrow’s spatial data processing solves this hallucination issue.

 

PaddleOCR 3.0: Supercharge Your AI

I upgraded to PaddleOCR 3.0 and explain the new PaddleOCR API integration. My goal is to integrate OCR result output with Vision LLM processing to enhance large-scale, structured table data output. 

 

Box Annotations in Sparrow for Structured Data Extraction

Check out my video on Box Annotations in Sparrow for Structured Data Extraction! I’ll show you how the Qwen2.5 vision model pulls bounding box annotations from images based on what you need. Plus, create simple descriptions and confidence score boxes. 

 

Structured Data Annotation with Qwen2.5 VL and MLX-VLM

Qwen2.5 VL can provide bounding box coordinates and confidence values for extracted structured data. This is useful for visual data review and reporting. I will explain with a practical example what prompt should be used to ensure Qwen2.5 returns this data. 

 

LLM Microservice with Instruction Calling

I describe the idea of implementing interaction with LLM through a concept of microservice with instruction calling. This works great for enterprise application use cases, such as data validation, workflor decisions.