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---
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license: mit
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task_categories:
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- image-to-text
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language:
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- en
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tags:
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- handwritten-digits
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- math-education
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- ocr
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- optical-character-recognition
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- handwriting-recognition
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size_categories:
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- n<1K
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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dataset_info:
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features:
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- name: session_id
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dtype: string
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- name: question_id
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dtype: string
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- name: timestamp
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dtype: string
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- name: operand_a
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dtype: int64
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- name: operand_b
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dtype: int64
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- name: operation
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dtype: string
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- name: correct_answer
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dtype: int64
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- name: difficulty
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dtype: string
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- name: ocr_prediction
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dtype: string
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- name: ocr_parsed_number
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dtype: int64
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- name: is_correct
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dtype: bool
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- name: ocr_model_name
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dtype: string
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- name: ocr_processing_time
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dtype: float64
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- name: ocr_confidence
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dtype: float64
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- name: session_duration
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dtype: int64
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- name: session_total_questions
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dtype: int64
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- name: app_version
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dtype: string
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- name: hardware
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dtype: string
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- name: handwriting_image
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dtype: image
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- name: session_accuracy
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dtype: float64
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- name: session_total_ocr_time
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dtype: float64
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- name: session_avg_ocr_time
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dtype: float64
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splits:
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- name: train
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num_bytes: 4394274.25
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num_examples: 1414
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download_size: 4239274
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dataset_size: 4394274.25
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---
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# CalcTrainer Dataset 🧮
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Handwritten mathematical answers collected from the [CalcTrainer](https://huggingface.co/spaces/hoololi/CalcTrainer) interactive math training application.
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## Dataset Fields
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### Core Data
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| Field | Type | Description |
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|-------|------|-------------|
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| `handwriting_image` | Image | Handwritten answer image (~100x100px) |
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| `ocr_prediction` | string | Raw OCR output text |
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| `ocr_parsed_number` | int32 | Cleaned numeric value from OCR |
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| `is_correct` | bool | Whether OCR matches correct answer |
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### Mathematical Context
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| Field | Type | Description |
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|-------|------|-------------|
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| `operand_a` | int32 | First number (e.g., 7 in "7 × 3") |
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| `operand_b` | int32 | Second number (e.g., 3 in "7 × 3") |
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| `operation` | string | Operation: `+`, `-`, `×`, `÷` |
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| `correct_answer` | int32 | Expected correct answer |
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| `difficulty` | string | `Facile` (Easy) or `Difficile` (Hard) |
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### OCR Metrics
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| Field | Type | Description |
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|-------|------|-------------|
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| `ocr_model_name` | string | OCR model used (e.g., "microsoft/trocr-base-handwritten") |
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| `ocr_processing_time` | float32 | Processing time in seconds |
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| `hardware` | string | Hardware used for OCR |
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### Session Info
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| Field | Type | Description |
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|-------|------|-------------|
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| `session_id` | string | Unique session identifier |
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| `question_id` | string | Unique question identifier |
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| `timestamp` | string | When the session was completed |
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| `session_duration` | int32 | Session length (30 or 60 seconds) |
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| `session_accuracy` | float32 | Overall session accuracy percentage |
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| `session_avg_ocr_time` | float32 | Average OCR time per image in session |
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## Usage
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```python
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from datasets import load_dataset
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dataset = load_dataset("hoololi/CalcTrainer_dataset")
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train_data = dataset["train"]
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# Example: Access first item
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item = train_data[0]
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print(f"Math problem: {item['operand_a']} {item['operation']} {item['operand_b']} = {item['correct_answer']}")
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print(f"OCR predicted: '{item['ocr_prediction']}' → {item['ocr_parsed_number']}")
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print(f"Correct: {item['is_correct']}")
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```
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## Data Source
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Real handwriting samples from users solving math problems in the CalcTrainer application. Users write answers on a digital canvas during timed math sessions.
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**Generated from**: [CalcTrainer Interactive Math Training](https://huggingface.co/spaces/hoololi/CalcTrainer) 🧮 |
