tesseract-ocrHow can I use a Long Short-Term Memory (LSTM) network to improve the accuracy of Tesseract OCR?

Long Short-Term Memory (LSTM) networks can be used to improve the accuracy of Tesseract OCR by providing a more accurate text recognition system. Tesseract OCR is a free and open-source OCR engine, but it can be improved by using a LSTM network.

For example, the following code block can be used to create a LSTM network and train it with Tesseract OCR data:

#import necessary libraries
import tensorflow as tf
from tensorflow.keras.layers import LSTM, Input, Dense

#define the input shape
inputs = Input(shape=(None, 128))

#define the LSTM network
lstm = LSTM(64, return_sequences=True)(inputs)

#define the output layer
outputs = Dense(128, activation='softmax')(lstm)

#compile the model
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

#train the model with Tesseract OCR data
model.fit(x_train, y_train, batch_size=32, epochs=10)

The code block above creates a LSTM network with an input shape of (None, 128), a LSTM layer with 64 units, and an output layer with 128 units and softmax activation. The model is then compiled using the adam optimizer and categorical_crossentropy loss function, and trained on Tesseract OCR data with a batch_size of 32 and 10 epochs.

This LSTM network can then be used to improve the accuracy of Tesseract OCR by providing a more accurate text recognition system.

Helpful links

• TensorFlow Keras LSTM Layer
• TensorFlow Keras Dense Layer
• Tesseract OCR

Edit this code on GitHub