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MathReader: API for handwritten mathematical expressions recognition

Paper: MathReader: API for handwritten mathematical expressions recognition, published in 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)

Details about implementation are described in the paper.

Interface for validation:

GitHub Project: MathReader Validation

MathReader Neural Network Training

If you want to re-train the neural network model. Feel free to improve it (details about the training process is described in the paper mentioned above).

GitHub Project: MathReader Training

About:

  • Accepted symbols:

    • {"0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "m", "n", "x", "y", "z", "+", "-", "*", "sqrt", "=", "neq"}

  • Internal modules:

    • Image preprocessing
    • Image posprocessing
    • Symbol recognition
    • Structural analysis
    • Lexical analysis
    • Sintatic analysis
    • Grammar correction

Usage:

Example in: main.py

from api import *

hme_recognizer = HME_Recognizer()

image = ['images/numbers/teste10.png']

try:
    hme_recognizer.load_image(image)
    expression, img = hme_recognizer.recognize()
    print("Latex: ", expression)
except Exception as e:
    print('Exception: ', e)

Configuration

Configuration file is in docs/config.json

{
    "application": {
        "debug_mode": "active",
        "debug_mode_image": "active"
    }
}
  • debug_mode: active activate debug messages in terminal
  • debug_mode_image: active show the images during the execution for debugging

HME_Recognizer

All attributes and methods avaiable in HME_Recognizer class at api.py

Attributes:

- image
- parsed_expression
- processed_image
- predictions
- configurations
- expression_after_recognition
- expression_after_parser
- expression_after_grammar
- parser_tree
- parser_list
- lex_errors
- yacc_errors
- pure_lex_errors
- pure_yacc_errors

Methods

  • load_image(image)

    • First method you should call.
    • It loads the image containing the expression.
    • Input: image.
    • Returns: None.

  • recognize()

    • Second method you should call.
    • It makes the recognition of the image containing the expression.
    • Input: None.
    • Returns:
      • expression: latex of the expression
      • image: image after processing

  • reset()

    • Reset all attributes from HME_Recognizer()
    • Input: None.
    • Returns: None

  • get_predictions()

    • Get all predictions from neural network
    • Input: None.
    • Returns: List of predictions.

  • get_expression_after_parser()

    • Get expression after going to parser module
    • Input: None.
    • Returns: (list) expression

  • get_expression_after_grammar()

    • Get expression after grammar check and correction
    • Input: None.
    • Returns: (str) expression

  • get_expression_after_recognition()

    • Get expression after recognition module
    • Input: None.
    • Returns: (dict) expression

  • get_configurations()

    • Get configuration from configuration file docs/config.json
    • Input: None.
    • Returns: configurations

  • get_lex_errors()

    • Get lex errors with a structure used by the grammar check

  • get_yacc_errors()

    • Get yacc errors with a structure used by the grammar check

  • get_pure_lex_errors()

    • Get 'pure' lex errors coming from PLY (Python Lex Yacc)

  • get_pure_yacc_errors()

    • Get 'pure' yacc errors coming from PLY (Python Lex Yacc)