Understanding Unicode Scripts in TensorFlow and Python

Rate this post

πŸ’‘ Problem Formulation: Developers working with text data in TensorFlow and Python often need to understand and manipulate Unicode scripts to handle internationalization and text processing accurately. For instance, when receiving text input in various languages, it’s necessary to process and convert into a uniform encoding before processing. The following methods illustrate how to work with Unicode scripts in TensorFlow and Python to maintain text integrity across different language scripts.

Method 1: Using Python’s str Functions for Unicode Normalization

Python’s standard library includes functions for Unicode normalization, which can be used to transform Unicode text into a consistent format. This is particularly useful for preparing text data before it’s fed into TensorFlow models. The unicodedata.normalize() function is a key feature utilized for this purpose, ensuring that text input is consistent.

Here’s an example:

import unicodedata

# Text with different forms of the letter 'Γ©'
text = u'Nai\u0301ve Caf\u00e9'

# Normalize the text to a standard form (NFC)
normalized_text = unicodedata.normalize('NFC', text)



NaΓ―ve CafΓ©

This code snippet normalizes the Unicode string text by using the NFC (Normalization Form Composed) standard, combining characters and diacritics into single characters when possible. The result is a consistently formatted version of the original text, which is crucial for text processing in Python and TensorFlow.

Method 2: Encoding Unicode Strings with TensorFlow Operations

TensorFlow provides functions to encode Unicode strings into a representation that can be processed by machine learning models. Using tf.strings.unicode_encode(), Unicode strings can be converted to UTF-8, making them compatible with the TensorFlow ecosystem.

Here’s an example:

import tensorflow as tf

# Define a Unicode string
text_tensor = tf.constant([u'δ½ ε₯½', u'μ•ˆλ…•ν•˜μ„Έμš”'])

# Encode the Unicode string to UTF-8
encoded_text = tf.strings.unicode_encode(text_tensor, 'UTF-8')



[b'\xe4\xbd\xa0\xe5\xa5\xbd' b'\xec\x95\x88\xeb\x85\x95\xed\x95\x98\xec\x84\xb8\xec\x9a\x94']

This code samples create a tensor with two Unicode strings, each in a different language, and then encodes them to UTF-8 using TensorFlow’s unicode_encode function. The output shows the encoded byte strings which can then be used by TensorFlow models for further processing.

Method 3: Decoding Byte Strings with TensorFlow Operations

Complementary to encoding, TensorFlow also provides operations for decoding byte strings back into human-readable Unicode text. The tf.strings.unicode_decode() function is used to convert byte strings, typically outputs from a model, back into Unicode strings.

Here’s an example:

import tensorflow as tf

# Byte string encoded in UTF-8
byte_strings = tf.constant([b'\xe4\xbd\xa0\xe5\xa5\xbd', b'\xec\x95\x88\xeb\x85\x95\xed\x95\x98\xec\x84\xb8\xec\x9a\x94'])

# Decode the byte strings to Unicode
decoded_text = tf.strings.unicode_decode(byte_strings, 'UTF-8')



[[ 20320  22909]
 [50504 45397 54616 49464 50836]]

This code takes a sequence of UTF-8 encoded byte strings and decodes them into their corresponding Unicode code points using TensorFlow’s unicode_decode() function. The resulting array contains the code points, which represent the original characters in the text tensors.

Method 4: Utilizing TensorFlow’s tf.strings.unicode_transcode() for Script Conversion

The tf.strings.unicode_transcode() function in TensorFlow allows for efficient transcoding between different Unicode encodings. This is essential when working with datasets containing different language scripts, ensuring they are in a format compatible with the model architecture.

Here’s an example:

import tensorflow as tf

# Define a UTF-8 encoded Unicode string
utf8_string = tf.constant([u'ΠŸΡ€ΠΈΠ²Π΅Ρ‚ ΠΌΠΈΡ€'.encode('UTF-8')])

# Transcode the string to UTF-16
utf16_string = tf.strings.unicode_transcode(utf8_string, input_encoding='UTF-8', output_encoding='UTF-16-BE')




In this example, a Russian phrase encoded in UTF-8 is transcoded to UTF-16 using TensorFlow’s unicode_transcode function. This operation is useful for converting between different Unicode representations based on model requirements or dataset specifications.

Bonus One-Liner Method 5: Direct Conversion of Python Unicode Strings to TensorFlow Tensors

For quick operations, you can directly convert Python Unicode strings into TensorFlow Tensors without specifying encoding transformations. This can be a straightforward approach for integrating native Python data types with TensorFlow operations.

Here’s an example:

import tensorflow as tf

# Directly convert Python Unicode strings to TensorFlow Tensors
tensor_from_strings = tf.constant([u'Hello, TensorFlow!', u'こんにけは'])



[b'Hello, TensorFlow!' b'\xe3\x81\x93\xe3\x82\x93\xe3\x81\xab\xe3\x81\xa1\xe3\x81\xaf']
shape=(2,), dtype=string)

This one-liner creates a TensorFlow tensor directly from a list of Python Unicode strings. The process automatically handles encoding the strings into the appropriate byte representation for TensorFlow.


  • Method 1: Unicode Normalization using unicodedata. Strengths: Easily accessible and part of Python’s standard library. Weaknesses: Not a TensorFlow-specific solution; additional steps may be required to convert to tensors.
  • Method 2: Encoding Unicode strings with TensorFlow. Strengths: Optimized for TensorFlow operations. Weaknesses: Requires familiarity with TensorFlow’s text processing functions.
  • Method 3: Decoding byte strings with TensorFlow. Strengths: Enables reverse operations of encoding for result interpretation. Weaknesses: May require additional error handling for unexpected encodings.
  • Method 4: Transcoding with tf.strings.unicode_transcode(). Strengths: Facilitates conversion between different Unicode encodings within TensorFlow. Weaknesses: More complex when dealing with numerous text encodings.
  • Bonus Method 5: Direct conversion to TensorFlow tensors. Strengths: Fast and convenient for native Python strings. Weaknesses: Lacks fine control over encoding details.