hoololi/CalcTrainer_dataset

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 🧮