CRNN Text Recognition Model Implemented with PaddlePaddle 2.0 Dynamic Graph
This document introduces a CRNN text recognition model implemented using PaddlePaddle 2.0 dynamic graph. The model extracts features through CNN, performs sequence prediction via RNN, and uses CTC Loss for loss calculation, making it suitable for input images of irregular lengths. **Training and Data Preparation:** 1. **Environment Configuration**: PaddlePaddle 2.0.1 and Python 3.7 need to be installed. 2. **Dataset Generation**: - Use the `create_image.py` script to automatically generate validation
Read MoreEnd-to-End Recognition of Captchas Based on PaddlePaddle 2.0
Your code has covered most aspects of the CAPTCHA recognition project, including data processing, model training, and inference. Below are some suggestions for improvements and enhancements to your provided code: ### 1. Data Preprocessing Ensure the image dimensions are consistent (27x72), as this is the input size used during training. ### 2. Model Definition Your `Model` class has already encapsulated the network structure well. You can further optimize it and add more comments to facilitate understanding. ### 3. Training Process During the training process, ensure that when using multi-GPU training,
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