Datasets:
metadata
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 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
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 🧮