HME_recognition

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HME_recognition

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Handwritten Math Expressions Recognition

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# HME_recognition
Handwritten Math Expressions Recognition

## Roadmap Outline
### Data Preparation
  - Refine on given training set
  - NIST – Refine (For English Characters)
  - CROHME (For math symbols): Online dataset to offline-like data : opencv.dilate + Gaussian blur

### Model
#### Classifier with following symbols:
  - Digits: 0-9
  - Characters: x, y, a, b, c, d, m, n, p, delta, f, h, k, sin, cos, tan, A, pi
  - Operators: +, -, \*, /, =, sqrt, ^, \_, bar, frac, cdots, (, )

[Source Code](/Model/keras_final32.py)g
  - Architecture: ResNet-50

#### Segmentation
  - opencv.findContours()

[Source Code](/Segmentation/cvFindContour.py)
  - Small window filters with various sizes to scan after opencv.findContours()
  - A Region Proposal-like Network for incompletely segmented parts

### RNN - Context
Merge decomposition parts of signs like =, /, cdots to one rectangle with RNN(LSTM)

### RNN
From symbol locations (4 points) and its label to adjust label and write to data structure

### Data structures and helpers
  - Tree-like ADT to hold math expressions
  - From ADT to latex

HME_recognition

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