---
language: th
tags:
- yolov8
- object-detection
- fintech
- ocr-pre-processing
- pytorch
- banking
license: mit
---

# 💳 Th-Slip-OCR-K: Precision Financial Detection

![Static Badge](https://img.shields.io/badge/Architecture-YOLOv8_Nano-blue?style=flat-square)
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**A lightweight, high-velocity object detection model engineered to extract full telemetry from Kasikorn Bank (K-Bank) transaction slips.**

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## ⚡ Overview
**Th-Slip-OCR-K** is a specialized computer vision model designed as a pre-processing engine for OCR pipelines. It isolates specific regions of interest within financial documents, eliminating noise and enabling higher accuracy for text extraction systems.

**🚀 V2 Update:** Enhanced detection capabilities to cover sender/receiver names and masked account numbers.

* **Architecture:** Ultralytics YOLOv8 Nano (v8.3.0)
* **Training Hardware:** Apple M4 Silicon (Metal Performance Shaders)
* **Optimization:** Low-latency inference, suitable for edge devices and mobile deployment.

## 🎯 Detection Classes (Updated)
The model is now trained to identify **6 critical zones** with high confidence:

| Label | Description | Context |
| :--- | :--- | :--- |
| **`amount`** | Numeric transaction value | Net transfer amount (e.g., 100.00) |
| **`accnum`** | Account Number | Supports masked formats (e.g., `xxx-2-x8574-x`) |
| **`date`** | Timestamp | Date and Time of transaction |
| **`pp1`** | Sender Name | Name of the person initiating the transfer |
| **`pp2`** | Receiver Name | Name of the beneficiary |
| **`transaction`** | Transaction Type | Operation status (e.g., Transfer Success) |

## 💻 Usage (Python)
Seamlessly integrate into your workflow using the `ultralytics` library.

```python
from ultralytics import YOLO

# 1. Load the model directly from Hugging Face Hub
model = YOLO("https://huggingface.co/Pondet/Th-Slip-OCR-K/resolve/main/best.pt")

# 2. Run Inference
results = model("path/to/your/slip.jpg")

# 3. Display Results
results[0].show()
```

## 📊 Performance Metrics
Evaluated on the latest K-Bank validation set (103 samples):

* **mAP50:** 0.995 (99.5%)
* **Precision:** 0.998 (High fidelity)
* **Recall:** 1.0 (Zero miss rate)

## ⚠️ Scope & Limitations
* **Domain Specific:** Optimized exclusively for Kasikorn Bank (Green Theme) slip layouts.
* **Privacy Aware:** The model detects masked account numbers (x) accurately but cannot reveal hidden digits without external database verification.
* **Educational Purpose:** Developed as a Proof-of-Concept for automated household accounting.

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Maintained by **Pondet**