PaddleOCR-VL-1.5 vs GLM-OCR: Local Test

PaddleOCR-VL-1.5 vs GLM-OCR | Ultimate 0.9B Open-Source Document VLM Comparison

Compare two of the most powerful, compact open-source OCR vision-language models available right now! In this video, I do a deep dive and head-to-head comparison of PaddleOCR-VL-1.5 by PaddlePaddle and GLM-OCR by Zhipu AI. Both models sit at around 0.9 billion parameters but are built with entirely different architectures to tackle complex document understanding, OCR, and data extraction tasks.

I put both models through a expected real world tests including messy receipts, math formulas, ID cards, street signs, code blocks, and badly lit text to see which local-friendly hybrid VLM reigns supreme for heavy OCR workloads.

What you’ll learn in this tutorial:
✅ The architectural differences between PaddleOCR-VL-1.5 and GLM-OCR.
✅ How to use GLM-OCR for structured data parsing using Custom JSON Schemas.
✅ Utilizing PaddleOCR's unique "Spotting" feature to draw bounding boxes around localized text.
✅ Real-world performance testing on receipts, tables, math formulas, and ID cards.
✅ Evaluating model behavior on tricky inputs like curved text, pie charts, and low-light images.
✅ A breakdown of parameter settings and which 0.9B model is the best choice for your automated OCR pipeline.

Tools & Models Used:

Gradio: For building the Web UI testing environment.
Transformers Library: Hugging Face backend framework for running the models.
PaddleOCR-VL-1.5: Compact 0.9B document VLM by PaddlePaddle.
GLM-OCR: Multimodal OCR model with a 0.5B language decoder by Zhipu AI.

PC Specs:

Gpu: Nvidia RTX 5060 Ti 16 GB : https://amzn.to/4rU7xRy
Ram: 64gb 4x16gb Kingston Fury : https://amzn.to/473HoaG

Model Used :

PaddleOCR-VL-1.5 (0.9B)
GLM-OCR (0.9B)

Pro Tip: Take advantage of GLM-OCR's custom JSON extraction schema for clean, automated data parsing in your pipelines, and use PaddleOCR's "Spotting" feature when you need precise text localization and bounding boxes!

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Timestamps:
0:00 - Intro & Overview
0:33 - PaddleOCR-VL-1.5 Specs & OmniDocBench Scores
1:04 - GLM-OCR Specs & Features
1:48 - Model Architecture Deep Dive
3:15 - Gradio Testing Setup & VRAM Usage
3:44 - PaddleOCR & GLM Parameters Explained
5:46 - Test 1: Receipt Extraction
7:30 - Test 2: CAPTCHA Recognition
8:06 - Test 3: Math Formula Recognition
9:10 - Test 4: Packaging Box Text
10:14 - Test 5: ID Card & JSON Schema Extraction
11:59 - Test 6: Meter Reading & Translation
12:45 - Test 7: Street Sign Text Spotting
14:05 - Test 8: Pie Chart Parsing
15:01 - Test 9: Seal & Stamp Recognition
15:31 - Test 10: Table Extraction
16:45 - Test 11: Curved Text on a Tire
19:01 - Test 12: Badly Lit Foreign Text
20:14 - Test 13: Code Block Syntax Preservation
20:58 - Final Thoughts & Conclusion

#GLMOCR #PaddleOCR #OCR #AIModels #MachineLearning #DocumentParsing #OpenSourceAI #ComputerVision #HuggingFace #VLM

Видео PaddleOCR-VL-1.5 vs GLM-OCR: Local Test канала kintu