# MicroOCR

**Repository Path**: william_lzw/micro-ocr

## Basic Information

- **Project Name**: MicroOCR
- **Description**: a micro OCR network.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 6
- **Forks**: 4
- **Created**: 2021-10-13
- **Last Updated**: 2021-12-09

## Categories & Tags

**Categories**: cv

**Tags**: None

## README

## MicroOCR
a micro OCR network.

This model can handle complex tasks without lstm, and its accuracy and speed are better than resnet and crnn models.

## Task Parameter Reference:
Simple recognition task:nh=32 or 64,depth=2

Chinese recognition task:nh=128 or 256,depth=2

Complex background recognition task:nh=512 or more,depth=2

## Model parameters
5000 training pictures, 500 verification pictures.
Nh    | Depth | Nclass  |    Params size  |Model size(KB)| Total train(epoch) | Word acc
:----:|:-----:|:-------:|:---------------:|:------------:|:------------------:|:--------:
16    |  2    |    62   |      9.726k     |      50      |        99          |    0.782
32    |  2    |    62   |      20.414k    |      93      |        100         |    0.842
64    |  2    |    62   |      44.862k    |      190     |        49          |    0.810
128   |  2    |    62   |      106.046k   |      434     |        45          |    0.882
256   |  2    |    62   |      277.566k   |      1113    |        50          |    0.872
512   |  2    |    62   |      817.214k   |      3239    |        45          |    0.884
1024  |  2    |    62   |      2.682942M  |      10563   |        49          |    0.894


## Script Description

```shell
MicroOCR
├── README.md                                   # descriptions about MicroNet
├── simsunb.ttf                                 # font file
├── collatefn.py                                # batch data processing
├── label_converter.py                          # label converter
├── dataset.py                                  # data preprocessing for training and evaluation
├── demo.py                                     # inference
├── gen_image.py                                # generate image for train and eval
├── infer_tool.py                               # inference tool
├── logger.py                                   # logger
├── keys.py                                     # character
├── loss.py                                     # ctcloss definition
├── metric.py                                   # accuracy metric for MicroNet
├── model.py                                    # MicroNet
├── train.py                                    # train the model
```

## Generate data for train and eval
```shell
python gen_image.py
```

## Training
```shell
python train.py
```

## Inference
```shell
python demo.py
```