---
library_name: transformers
tags:
- text-classification
- korean
- ocr
- screenshot-classification
- klue-roberta
- capturemate
---

# CaptureMate Category Classifier v2 4-Class

OCR text-based screenshot category classifier for CaptureMate.

This model classifies OCR-extracted screenshot text into one of four app categories: `schedule`, `shopping`, `place`, and `memo`.

`unknown` samples are excluded from training. Ambiguous or low-confidence predictions should be handled by the application using a confidence threshold.

## Model Details

### Model Description

This model is a fine-tuned KLUE-RoBERTa based text classification model for CaptureMate, an iOS screenshot organization and action recommendation app.

The model receives OCR text extracted from screenshots and predicts the most relevant screenshot category.

- **Developed by:** CaptureMate
- **Model type:** Text classification
- **Language(s):** Korean, English, mixed OCR text
- **License:** Not specified
- **Finetuned from model:** KLUE-RoBERTa base
- **Number of labels:** 4

### Labels

| ID | Label | Description |
|---:|---|---|
| 0 | schedule | Schedule, reservation, ticket, event, or date-related screenshots |
| 1 | shopping | Shopping, product, price, payment, or commerce-related screenshots |
| 2 | place | Place, map, restaurant, store, travel, or location-related screenshots |
| 3 | memo | Text, article, note, content, or general information screenshots |

## Uses

### Direct Use

Use this model to classify OCR-extracted screenshot text into CaptureMate categories.

### Downstream Use

This model can be used inside a screenshot processing pipeline:

```txt
Screenshot
-> OCR text extraction
-> Text preprocessing
-> Category classification
-> Category-specific action recommendation
```

## Out-of-Scope Use

This model is not designed to:

- Classify original images directly
- Perform OCR
- Extract structured fields such as dates, prices, addresses, or product names
- Predict an `unknown` category directly
- Classify content outside the CaptureMate screenshot domain

## Bias, Risks, and Limitations

The model is trained on a small CaptureMate-specific screenshot OCR dataset. It may not generalize well to unrelated domains or OCR text from different user behavior patterns.

Known limitations:

- The model always predicts one of the four labels.
- There is no direct `unknown` output.
- Very short or noisy OCR text can lead to unstable predictions.
- Ambiguous cases such as ticket screenshots, food blog screenshots, or shopping-like memo content may be confused.

### Recommendations

Use a confidence threshold at inference time.

Example:

```txt
if confidence < 0.6:
    category = "unknown"
else:
    category = predicted_label
```

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "YOUR_USERNAME/capturemate-category-classifier-v2-4class"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "2025.4.7 티켓 수령 공연 예약 정보"

inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    max_length=256
)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=-1)
    pred_id = probs.argmax(dim=-1).item()
    confidence = probs[0][pred_id].item()

label = model.config.id2label[pred_id]

print(label, confidence)
```

## Training Details

### Training Data

The model was trained on OCR text extracted from screenshot images collected for the CaptureMate project.

Dataset split:

| Split | Samples |
|---|---:|
| Train | 340 |
| Validation | 73 |
| Test | 74 |

`unknown` samples were removed before creating the final training, validation, and test splits.

### Training Procedure

#### Preprocessing

- OCR text was extracted from screenshots.
- Empty text samples were removed.
- Labels outside the four target categories were excluded.
- Text was tokenized with the KLUE-RoBERTa tokenizer.
- Maximum sequence length: 256 tokens.

#### Training Hyperparameters

| Hyperparameter | Value |
|---|---:|
| Learning rate | 1e-5 |
| Epochs | 8 |
| Train batch size | 16 |
| Eval batch size | 16 |
| Warmup ratio | 0.1 |
| Weight decay | 0.01 |
| Best model metric | macro F1 |
| Seed | 42 |

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The test set contains 74 OCR text samples across four categories.

#### Metrics

The model was evaluated using:

- Accuracy
- Macro F1
- Macro precision
- Macro recall

Macro metrics are important because the dataset is not perfectly balanced across categories.

### Results

| Metric | Validation | Test |
|---|---:|---:|
| Accuracy | 98.63% | 94.59% |
| Macro F1 | 98.89% | 94.55% |
| Macro Precision | 99.07% | 93.91% |
| Macro Recall | 98.75% | 95.47% |

### Test Set Per-Class Results

| Class | Precision | Recall | F1-score | Support |
|---|---:|---:|---:|---:|
| schedule | 90.00% | 100.00% | 94.74% | 9 |
| shopping | 96.15% | 96.15% | 96.15% | 26 |
| place | 94.74% | 100.00% | 97.30% | 18 |
| memo | 94.74% | 85.71% | 90.00% | 21 |

### Confusion Matrix

Rows are true labels, columns are predicted labels.

| True \ Pred | schedule | shopping | place | memo |
|---|---:|---:|---:|---:|
| schedule | 9 | 0 | 0 | 0 |
| shopping | 0 | 25 | 0 | 1 |
| place | 0 | 0 | 18 | 0 |
| memo | 1 | 1 | 1 | 18 |

## Technical Specifications

### Model Architecture and Objective

- Architecture: KLUE-RoBERTa sequence classification
- Objective: Single-label multi-class classification
- Input: OCR text
- Output: One of four CaptureMate categories

### Software

- transformers
- datasets
- torch
- scikit-learn

## Model Card Authors

CaptureMate

## Model Card Contact

CaptureMate project maintainer