Project SEM 6

Handwritten text recognition

What ?

Handwritten text recognition using Long Short Term Memory implementation of RNN. Honestly I don't know what's happening here. The IAM Handwriting Database is used to train the network. Specifically the words dataset.

How to ?

Clone the repo and then do the following.

Get the IAM Handwriting Dataset :

• Make an account on the database website.
• Download the words archive
• Extract the contents to the following directory structure on the root of this project : Datasets/words
  • So the resulting folder structure should be :

    Datasets
        |_words
            |_a01
                |_a01-000u
                    |_images.png
                |_a01-000x
                |_...
            |_a02
            |_...
    

• Now you need the labels for these images.
• Download the ascii archive
• Extract only the words.txt file to the Datasets folder.

Install the dependencies

• install the requirements.
• Do in terminal : pip install -r requirements.txt
• The required packages are :
  • numpy : 1.22.4
  • opencv_python : 4.6.0.66
  • pandas : 1.3.2
  • Pillow : 9.4.0
  • tensorflow : 2.10.0
  • tqdm : 4.62.3

Edit the configurations

• Open the config.py file.
• Make whatever changes you want.
• Mainly you might want to make changes to the following variables :
  • batch_size
  • learning_rate
  • train_epochs
  • train_workers
  • validation_split

Train the model

• Run the train.py script to train the model.
• No need for editing anything in this file.
• I trained the model with the following parameters :
  • learning rate : 0.0005
  • validation split : 0.9
  • batch size = 16
  • epochs = 100
• I trained it with my GTX 1650. It used 2132 MB of GPU memory. Usage was around 8-10 %. Took around 140-150 seconds each epoch. Took about 4 hours for all 100 epochs.
• CPU usage was around 50%. My CPU is Ryzen 7 3750H.
• RAM usage was around 2 GBs.
• You can visualise the training using tensorboard. Run tensorboard --logdir = path_to_logs in terminal to start the server.
• The logs are located at the following folder : Models/Handwriting_recognition/{timestamp}/logs
• After training, a bunch of files are generated. The only important files are model.meow and configs.meow.

Run the model

• You can run the inferenceModel.py to check with only one image from the dataset itself.
• OR use the Paint GUI made with tkinter by running the tkRecog.py.

IN BOTH CASES MAKE SURE TO EDIT THE unixTime VARIABLE WITH YOUR MODEL'S FOLDER

Thank You

Totally not copied code.