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TFSeqRec: A TF2 Toolkit for Sequence Text Recognition

This package provides a comprehensive set of tools and functionalities to facilitate the model development, training, and evaluation of Text Recognition, a sub-task of Optical Character Recognition (OCR).

  • This package is particularly designed to handle this Text Recognition task at the Sequence level, where the input and the output are a sequence of characters or tokens.
  • It is built on top of TensorFlow 2 with the aim to simplify the process of importing, handling, and visualizing sequence data, as well as providing common loss functions and evaluation metrics often used in the development of Sequence-level Text Recognition models.
pip install tfseqrec

Quick Start

The demo folder contains 2 Jupyter notebooks that demonstrate how to use this toolkit:

Key Features and Functionalities

This package is organized into several modules, each of which provides a set of classes/functions to handle a specific aspect of the Sequence-level Text Recognition implementation.

Table of Contents:

I. tfseqrec.loader 🔝

1️⃣ class DataImporter (dataset_dir, labels_path, min_length=4, filter_func=None) 🔝:

A class for importing, filtering valuable image, and text data for Sequence-level Text Recognition models.

  • dataset_dir (str): The directory path where the dataset is located. This is to construct full paths to images defined in the file in labels_path.
  • labels_path (str): The path to the file containing the labels for the dataset. This file should contain lines in this format: image_name.jpg,text_label.
  • min_length (int): The minimum length of a label to be considered valid.
  • filter_func (callable): Additional function to further filter labels. Only labels that pass the filter will be included in the dataset. This will remove corresponding images as well.

Other Attributes:

  • img_paths (list): A list of file paths to images in the dataset.
  • labels (list): A list of labels corresponding to these images.
  • vocabs (set): A set of unique characters present in the labels.
  • size (int): The number of items in the dataset.

For each line of the file located in the labels_path, it extracts the full image path and the associated text/label. If the image file exists, the text length is at least min_length, and the text/label passes the filter_func (if provided), the full image path and the text will be added to img_paths and labels, respectively. Finally, the vocabs or the frequency of each character in the labels will be computed.

👉 remove_rare_chars (threshold=2) -> DataImporter 🔝:

Remove items from img_paths and labels that contains characters appearing less than a certain threshold.

  • threshold (int): The minimum frequency of a character to be considered as not rare. Must be >= 2.

return: The updated DataImporter object with rare characters removed.

It identifies rare characters and the indices of labels containing them by iterating over the vocabs from least to most common. Then, it removes these labels and their corresponding images, recalculates the vocabs, and updates the size. If there are still rare characters in the labels, it recursively calls itself with the same threshold until all rare characters are removed.

👉 str() -> str 🔝:

f'- Number of images found: {len(img_paths)}\n'
f'- Number of labels found: {len(labels)}\n'
f'- Number of unique characters: {len(vocabs)}\n'
f'- Characters present: {vocabs}'

return: A string representation of the DataImporter object.

This method can be called when the print() or the str() function is used on a DataImporter object.

2️⃣ class DataHandler (dataset, img_size, padding_char, start_char='', end_char='') 🔝:

A class for handling, preprocessing image, and text data for Sequence-level Text Recognition models.

  • dataset (DataImporter): The DataImporter object containing the dataset.
  • img_size (tuple): The desired size of the input images.
  • padding_char (str): The character used for padding labels.
  • start_char (str): The character used to indicate the start of a label/sentence.
  • end_char (str): The character used to indicate the end of a label/sentence.

Other Attributes:

  • img_paths (list): A list of file paths to the images in the dataset.
  • labels (list): A list of labels corresponding to the images.
  • vocabs (set): A set of unique characters present in the labels.
  • size (int): The number of items in the dataset.
  • max_length (int): The maximum length of the labels in the dataset.
  • token_mask (dict): A dictionary mapping characters to their corresponding indices.
  • char2num (tf.keras.layers.StringLookup): TF2 preprocessing layer that maps strings to (possibly encoded) indices/tokens.
  • num2char (tf.keras.layers.StringLookup): The inverse of char2num that maps indices/token IDs back to strings.
  • start_token (int): The index from char2num corresponding to the start_char if it is defined.
  • end_token (int): The index from char2num corresponding to the end_char if it is defined.

👉 distortion_free_resize (image, align_top=True) -> tf.Tensor 🔝:

Resize an image while preserving its aspect ratio and applying distortion-free padding. If the resized image doesn't match the targeted img_size, the method will pad the image with white color to reach that size.

  • image (tf.Tensor): The input image tensor.
  • align_top (bool): Whether to align the image to the top or center it vertically.

return: The resized and padded image tensor.

👉 process_image (img_path, img_align_top=True, channels=3) -> tf.Tensor 🔝:

Preprocess an image by reading it from file, resizing it using the distortion_free_resize method, and normalizing its pixel values to the range [0, 1].

  • img_path (str): The path to the image file.
  • img_align_top (bool): Whether to align the image to the top or center it vertically during resizing.
  • channels (int): The number of color channels in the image.

return: The preprocessed image tensor.

👉 process_label (label) -> tf.Tensor 🔝:

Preprocess a label (a sequence of characters) by converting it into a sequence of integer tokens using the char2num mapping. It also adds start_token and end_token if they are available, and pads the sequence with the padding token to match the max_length.

  • label (str): The input label.

return: The preprocessed label tensor.

👉 prepare_tf_dataset (idxs, batch_size, channels=3, drop_remainder=False, img_align_top=True, use_cache=True) -> tf.data.Dataset 🔝:

This is an all-in-one method that combines all preprocessing steps above to create a ready-to-use TensorFlow dataset for training or evaluation from the img_paths and labels obtained from the DataImporter object. It applies the process_image and process_label methods to each image-label pair in parallel using TensorFlow's AUTOTUNE setting. The dataset is batched, optionally cached in memory for faster access, and prefetched for performance.

  • idxs (np.ndarray): The indices of the samples to include in the dataset.
  • batch_size (int): The batch size.
  • channels (int): The number of color channels in the images.
  • drop_remainder (bool): Whether to drop the last batch if it has fewer samples than the batch_size.
  • img_align_top (bool): Whether to align the images to the top or center them vertically during resizing.
  • use_cache (bool): Whether to cache the dataset in memory for faster training. If True, everything before is saved in the memory. It gives a significant boost in speed but only if you can get your hands on a larger RAM.

return: The prepared TensorFlow dataset.

👉 tokens2texts (batch_tokens, use_ctc_decode=False) -> List[str] 🔝:

Converts a batch of token sequences back to text using the num2char mapping. The start_char and end_char will be also removed if they are available.

  • batch_tokens (tf.Tensor): The batch of token sequences.
  • use_ctc_decode (bool): Whether to use Connectionist Temporal Classification (CTC) decoding to convert the tokens to text. This is often used in CRNN model.

return: A List[str] of texts sequences corresponding to the tokenized predictions.

II. tfseqrec.losses 🔝

1️⃣ class CTCLoss (name='ctc_loss', **kwargs) 🔝:

A custom TensorFlow 2 loss for computing the Connectionist Temporal Classification (CTC) loss between the true labels and the predicted logits. This is often used in CRNN model.

  • This custom loss allows for easy integration of CTC loss into TensorFlow models, especially when using the Keras API, by simply instantiating this class and using it as the loss function during model compilation.
  • It uses TensorFlow's built-in ctc_batch_cost function, which is responsible for calculating the CTC loss for a batch of sequences.

👉 Note: This class is deprecated as TensorFlow now has its own built-in support for CTC loss. You can use this tf.keras.losses.CTC class directly in your model compilation.

2️⃣ class MaskedLoss (name='masked_loss', **kwargs) 🔝:

A custom TensorFlow 2 loss for computing the loss between the true labels and the predicted logits of variable-length sequences, where padding tokens are used to make all sequences of equal length.

  • This custom loss is designed to work with models that output predictions for sequences where some values might be ignored during the loss calculation, such as in sequence modeling tasks where padding tokens are used, typically represented by 0.

  • The main part of this class is the SparseCategoricalCrossentropy as the loss function to be used. The model outputs are expected to be logits, and the labels are expected to be provided as integers (sparse labels).

👉 The class calculates the loss only for the non-padding tokens, effectively masking out the padding tokens from the loss calculation.

III. tfseqrec.metrics 🔝

1️⃣ class SequenceAccuracy (use_ctc_decode=False, name='seq_acc', **kwargs) 🔝:

A custom TensorFlow 2 metric for use with TensorFlow's Keras API to evaluate the accuracy of sequence predictions, with support for both regular and CTC-decoded predictions. It computes the accuracy of a given set of predicted sequences of tokens compared to their corresponding true sequences.

Attributes:

  • use_ctc_decode (bool): Whether to decode predictions. This is necessary when the model's output is encoded with CTCLoss, such as in CRNN.
  • total (tf.Variable): Variable to keep track the total number of predictions considered across batches.
  • count (tf.Variable): Variable to keep track the number of correct predictions across batches (total - errors).

Methods:

  • update_state(y_true, y_pred, **kwargs):
    • The method is called during training to accumulate the metric's state with new observations over multiple batches of true labels (y_true) and predicted labels (y_pred).
    • If use_ctc_decode is True, the predictions (y_pred) are decoded using the ctc_decode function.
  • result(): Compute and returns the accuracy as the ratio of accumulated count to total (count / total).
  • reset_state(): Reset the count and total to 0. This is typically called at the start of each epoch during training to clear the metric's state for the new epoch's calculations

2️⃣ class CharacterAccuracy (use_ctc_decode=False, name='char_acc', **kwargs) 🔝:

A custom TensorFlow 2 metric for detailed monitoring of model performance on a character-by-character basis in use with TensorFlow's Keras API. It computes the accuracy of individual characters in the predicted sequences compared to their corresponding true sequences.

Attributes and Methods: similar to the above SequenceAccuracy class but at the character level.

3️⃣ class LevenshteinDistance (use_ctc_decode=False, normalize=False, name='levenshtein_distance', **kwargs) 🔝:

A custom TensorFlow 2 metric for use with TensorFlow's Keras API to compute the Levenshtein distance (edit distance) between the predicted logits and their corresponding true tokens. It measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to change a word into another.

Attributes:

  • use_ctc_decode (bool): Whether to decode predictions if CTCLoss is used.
  • normalize (bool): If True, the calculation will be adjusted to return the Character Error Rate (CER), which is the distance normalized by the length of the true sequence, instead of the raw edit distance.: $$CER = \frac{S(substitutions) + D(deletions) + I(insertions)}{N\text{(length or total chars in the true label)}}$$
  • sum_distance (tf.Variable): Variable to accumulate or keep track the sum of distances.
  • total (tf.Variable): Variable to keep track the total number of predictions considered across batches.

Methods:

  • update_state(y_true, y_pred, **kwargs):
    • It uses the tf.edit_distance function to calculate the edit distance for each y_true & y_pred pair in a batch, accumulating the sum_distance (with these distances) and the total of sequences (with the current batch_size).
    • If use_ctc_decode is True, the predictions (y_pred) are decoded using the ctc_decode function.
  • result(): Compute the final metric value, which is the average Levenshtein distance (or CER if normalized) across all sequences processed (sum_distance / total).
  • reset_state(): Reset the count and total to 0. This is typically called at the start of each epoch during training to clear the metric's state for the new epoch's calculations.

IV. tfseqrec.visualizer 🔝

1️⃣ class SequenceVisualizer (fontsize=12, fontpath=None) 🔝:

This class is to visualize text sequences, particularly focusing on comparing predicted labels against actual labels in a visual manner. It will annotate mismatches and provide visual cues for correct and incorrect predictions.

  • (Parameter) fontpath (str): Path to the custom fonts used for text annotation.
  • (Attribute) fontdict (dict): Styling information for the text to be drawn. It includes fontsize and fontpath if provided.

👉 draw_predicted_text (label, pred_label, width, height) 🔝:

Draw the predicted text on an image, indicating matches & mismatches with actual label by different colors.

  • label (str): The actual label text.
  • pred_label (str): The predicted label text.
  • width (int): Width of the image.
  • height (int): Height of the image.

This function compares a pred_label against actual label, both provided as strings, and visualizes the comparison:

  • If there are differences, it iterates through the characters of the label, comparing substrings to find matches or mismatches with the pred_label.
  • It can handle different scenarios of mismatches, such as missing positions or wrong predictions, and adjusts the text color & position to visually indicate the type of error/mismatch.
  • The position of each character/token is calculated based on the label's length and the provided dimensions (width, height), aiming to distribute the text evenly across the available space.
  • It decides the orientation of the text based on whether height >= width and formats the text accordingly using the fontdict.

👉 plot_images_labels (img_paths, labels, pred_labels=None, figsize=(15, 8), subplot_size=(2, 8), legend_loc=None, annotate_loc=None) 🔝:

Plot a batch/series of images with their corresponding labels and optionally their predicted labels.

  • img_paths (list): List of image file paths.
  • labels (list): List of actual labels.
  • pred_labels (list): List of predicted labels.
  • figsize (tuple): Size of the figure.
  • subplot_size (tuple): The layout of subplots in terms of rows and columns.
  • legend_loc (str): Location for the legend.
  • annotate_loc (tuple): Location for the annotation.

For each image, it reads the image file and plots it along with its actual labels. If the pred_labels are provided, it will call the draw_predicted_text method above to visualize the comparison between actual and predicted labels.

This method also supports adding a legend and annotations if legend_loc, annotate_loc and pred_labels are available, explaining the meaning of different colors used in the visualization (e.g., green for full matches, red for wrong predictions) and listing the model predictions.

2️⃣ function plot_training_results (history, save_name, figsize=(16, 14), subplot_size=(2, 2)) 🔝:

This function generates plots for losses and metrics of training and validation from a model's training history. It visualizes the performance of the model over epochs and saves the plot as an image.

  • history (dict): A dictionary containing the training history from model.fit(). Keys should be metric names (e.g., 'loss', 'accuracy'), and their values should be lists of metric values over epochs. Keys prefixed with val_ represent validation metrics.
  • save_name (str): The name of the file to save the plot.
  • figsize (tuple): The size of the figure.
  • subplot_size (tuple): The layout of subplots in terms of rows and columns.

V. tfseqrec.utils 🔝

1️⃣ function ctc_decode (predictions, max_length) -> tf.Tensor 🔝:

It uses TensorFlow's built-in ctc_decode function to decode predictions from a model that has been trained using the Connectionist Temporal Classification (CTC) loss function.

  • predictions (tf.Tensor): The prediction probabilities from the model, typically of shape (batch_size, time_steps, num_classes).
  • max_length (int): The maximum length of the decoded sequences.

return: A tensor containing the decoded sequences with shape (batch_size, max_length), where the [UNK] token is treated the same as the blank label and is replaced by -1.

2️⃣ function tokens2sparse (batch_tokens) -> tf.SparseTensor 🔝:

Convert a dense batch of token sequences into a sparse tensor format, ignoring padding tokens & blank labels.

  • batch_tokens (tf.Tensor): A tensor of token sequences with shape (batch_size, sequence_length). 0 tokens represent padding ([PAD]), and -1 tokens represent blank labels.

return: A sparse representation of the input token sequences.

3️⃣ function sparse2dense (tensor, shape) -> tf.Tensor 🔝:

Convert a sparse tensor back into a dense tensor format with a specified shape.

Parameters:

  • tensor (tf.SparseTensor): The input sparse tensor to be converted.
  • shape (tuple or list): The desired shape for the output dense tensor.

return: The dense representation of the input sparse tensor, with shape specified by the shape parameter and missing entries filled with -1.