---
license: mit
task_categories:
- image-to-text
language:
- en
tags:
- handwritten-digits
- math-education
- ocr
- optical-character-recognition
- handwriting-recognition
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: session_id
    dtype: string
  - name: question_id
    dtype: string
  - name: timestamp
    dtype: string
  - name: operand_a
    dtype: int64
  - name: operand_b
    dtype: int64
  - name: operation
    dtype: string
  - name: correct_answer
    dtype: int64
  - name: difficulty
    dtype: string
  - name: ocr_prediction
    dtype: string
  - name: ocr_parsed_number
    dtype: int64
  - name: is_correct
    dtype: bool
  - name: ocr_model_name
    dtype: string
  - name: ocr_processing_time
    dtype: float64
  - name: ocr_confidence
    dtype: float64
  - name: session_duration
    dtype: int64
  - name: session_total_questions
    dtype: int64
  - name: app_version
    dtype: string
  - name: hardware
    dtype: string
  - name: handwriting_image
    dtype: image
  - name: session_accuracy
    dtype: float64
  - name: session_total_ocr_time
    dtype: float64
  - name: session_avg_ocr_time
    dtype: float64
  splits:
  - name: train
    num_bytes: 4495607.125
    num_examples: 1447
  download_size: 4332050
  dataset_size: 4495607.125
---

# CalcTrainer Dataset 🧮

Handwritten mathematical answers collected from the [CalcTrainer](https://huggingface.co/spaces/hoololi/CalcTrainer) interactive math training application.

## Dataset Fields

### Core Data
| Field | Type | Description |
|-------|------|-------------|
| `handwriting_image` | Image | Handwritten answer image (~100x100px) |
| `ocr_prediction` | string | Raw OCR output text |
| `ocr_parsed_number` | int32 | Cleaned numeric value from OCR |
| `is_correct` | bool | Whether OCR matches correct answer |

### Mathematical Context
| Field | Type | Description |
|-------|------|-------------|
| `operand_a` | int32 | First number (e.g., 7 in "7 × 3") |
| `operand_b` | int32 | Second number (e.g., 3 in "7 × 3") |
| `operation` | string | Operation: `+`, `-`, `×`, `÷` |
| `correct_answer` | int32 | Expected correct answer |
| `difficulty` | string | `Facile` (Easy) or `Difficile` (Hard) |

### OCR Metrics
| Field | Type | Description |
|-------|------|-------------|
| `ocr_model_name` | string | OCR model used (e.g., "microsoft/trocr-base-handwritten") |
| `ocr_processing_time` | float32 | Processing time in seconds |
| `hardware` | string | Hardware used for OCR |

### Session Info
| Field | Type | Description |
|-------|------|-------------|
| `session_id` | string | Unique session identifier |
| `question_id` | string | Unique question identifier |
| `timestamp` | string | When the session was completed |
| `session_duration` | int32 | Session length (30 or 60 seconds) |
| `session_accuracy` | float32 | Overall session accuracy percentage |
| `session_avg_ocr_time` | float32 | Average OCR time per image in session |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("hoololi/CalcTrainer_dataset")
train_data = dataset["train"]

# Example: Access first item
item = train_data[0]
print(f"Math problem: {item['operand_a']} {item['operation']} {item['operand_b']} = {item['correct_answer']}")
print(f"OCR predicted: '{item['ocr_prediction']}' → {item['ocr_parsed_number']}")
print(f"Correct: {item['is_correct']}")
```

## Data Source

Real handwriting samples from users solving math problems in the CalcTrainer application. Users write answers on a digital canvas during timed math sessions.

**Generated from**: [CalcTrainer Interactive Math Training](https://huggingface.co/spaces/hoololi/CalcTrainer) 🧮