Datasets:
File size: 2,637 Bytes
4921bca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
license: mit
task_categories:
- image-to-text
- optical-character-recognition
language:
- en
tags:
- handwritten-digits
- math-education
- ocr
size_categories:
- n<1K
---
# 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) 🧮 |