paddle ocr training digit recognition model

Choose a recognition algorithm

gitlab

Modify the configuration file

Copy the rec_icdar15_train.yml configuration file, the pre-training model uses rec_mv3_none_bilstm_ctc

Change

• pretrained_model pre-trained model path
• character_dict_path dictionary path, dictionary content
• data_dir
• label_file_list
  

Global:
  use_gpu: true
  epoch_num: 10
  log_smooth_window: 20
  print_batch_step: 10000
  save_model_dir: ./output/rec/number/
  save_epoch_step: 5
  # evaluation is run every 2000 iterations
  eval_batch_step: [0, 3, 6, 9]
  cal_metric_during_train: True
  pretrained_model: pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy
  checkpoints:
  save_inference_dir: ./
  use_visualdl: False
  infer_img: ./train_data/NUMBER/9997_448.jpg
  # for data or label process
  character_dict_path: ppocr/utils/number_dict.txt
  max_text_length: 6
  infer_mode: False
  use_space_char: True
  save_res_path: ./output/rec/predicts_number.txt

Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  lr:
    learning_rate: 0.0005
  regularizer:
    name: 'L2'
    factor: 0

Architecture:
  model_type: rec
  algorithm: CRNN
  Transform:
  Backbone:
    name: MobileNetV3
    scale: 0.5
    model_name: large
  Neck:
    name: SequenceEncoder
    encoder_type: rnn
    hidden_size: 96
  Head:
    name: CTCHead
    fc_decay: 0

Loss:
  name: CTCLoss

PostProcess:
  name: CTCLabelDecode

Metric:
  name: RecMetric
  main_indicator: acc

Train:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/NUMBER/
    label_file_list: ["./train_data/NUMBER/rec_gt_train.txt"]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - CTCLabelEncode: # Class handling label
      - RecResizeImg:
          image_shape: [3, 32, 100]
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
  loader:
    shuffle: True
    batch_size_per_card: 256
    drop_last: True
    num_workers: 12
    use_shared_memory: False

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/NUMBER
    label_file_list: ["./train_data/NUMBER/rec_gt_test.txt"]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - CTCLabelEncode: # Class handling label
      - RecResizeImg:
          image_shape: [3, 32, 100]
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
  loader:
    shuffle: False
    drop_last: True
    batch_size_per_card: 256
    num_workers: 4
    use_shared_memory: False

file format

The simple dataset splits with \t

to generate labels for images

import os
import cv2

from tqdm import tqdm

img_folder = r'xxx'
target_img_folder = r'./train_data' 
img_file_list = os.listdir(img_folder)

label_list = []


def cv_show(img):
    '''
    展示图片
    @param img:
    @param name:
    @return:
    '''
    cv2.namedWindow('name', cv2.WINDOW_KEEPRATIO)  # cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO
    cv2.imshow('name', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

index = 1

for file in tqdm(img_file_list):
    file_path = os.path.join(img_folder, file)
    start_page_str = str(index)
    if len(start_page_str) == 1:
        start_page_str = '00' + start_page_str
    elif len(start_page_str) == 2:
        start_page_str = '0' + start_page_str
    else:
        ...
    if file.endswith('jpg'):
        label = file.split('_')[-1].split('.')[0]
        new_file_path =  os.path.join(target_img_folder, str(start_page_str) +'_'+label+ '.jpg')
        os.rename(file_path,new_file_path)

        with open('./rec_gt_train.txt', 'a+', encoding='utf-8') as f:
            f.write(str(start_page_str) +'_'+label+ '.jpg'+'\t'+label+'\n')
        index += 1

dataset

dataset:
    name: SimpleDataSet
    data_dir: ./train_data/NUMBER
    label_file_list: ["./train_data/NUMBER/xxx.txt"]

train

python tools/train.py -c configs/rec/rec_icdar15_number_train.yml

There are 30,000 pictures, using the pre-training model, the effect of the pre-training model:

Without using the training model, the accuracy will be almost

python tools/train.py -c configs/rec/rec_r34_vd_none_bilstm_ctc_number.yml

into an inference model

python tools/export_model.py -c configs/rec/rec_icdar15_number_train.yml -o Global.checkpoints=./output/rec/number_mv3/best_accuracy Global.save_inference_dir=./output/rec_icdar15_number/

warn:

The shape of model params head.fc.weight [192, 12] not matched with loaded params head.fc.weight [192, 37]

because the dictionary changed

Notice

• The test set and training set size is larger than the batch

predict

Predicted image:

Command Line:

python ../../PaddleOCR/tools/infer/predict_rec.py --image_dir="./test_data/000_4.jpg" --rec_model_dir="../../PaddleOCR/output/recnumber_mv3_none_bilstm_ctc/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="../../PaddleOCR/ppocr/utils/number_dict.txt"

original model
paddleocr --image_dir="./test_data/000_4.jpg"

Summarize

The effect of using different image sizes will be worse, and data enhancement is required during training