attention-ocr

Introduction to Attention-based OCR Project

The Attention-based Optical Character Recognition (OCR) project is a sophisticated visual model designed to translate images into textual data effectively. This model incorporates a combination of cutting-edge technologies, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms, to perform accurate image recognition tasks. Additionally, the project is equipped with tools for creating TFRecords datasets and exporting trained models as TensorFlow's SavedModel or frozen graph formats.

Model Origin and Development

The core of this project is built on the foundations laid by researchers Qi Guo and Yuntian Deng, whose expertise is encapsulated in the original Attention-OCR repository. The approach leverages a sliding CNN that processes images resized to a standardized height while maintaining the aspect ratio. On top of this CNN, an LSTM layer is applied, and the whole system is crowned by an attention-based decoder, which refines the output into coherent text.

Installation

To utilize this project, users can easily install the necessary package by executing:

pip install aocr

The installation automatically handles core dependencies such as TensorFlow and NumPy, with additional reliance on packages like Pillow, distance, and six. It's important to note that the project is initially compatible with TensorFlow 1.x, with ongoing efforts to upgrade to TensorFlow 2. Contributions to this transition are welcome through pull requests.

Creating a Dataset

For the model to learn effectively, a dataset containing images and their corresponding labels is essential. These datasets are transformed into TFRecords, a format optimized for TensorFlow.

Commands to create datasets include:

aocr dataset ./datasets/annotations-training.txt ./datasets/training.tfrecords
aocr dataset ./datasets/annotations-testing.txt ./datasets/testing.tfrecords

Annotation files are straightforward text files where each line pairs an image path with its respective label.

Training the Model

Training the model involves using the command:

aocr train ./datasets/training.tfrecords

This initiates a training session where both the CNN and attention model are fine-tuned, a process that may take a significant amount of time for convergence. Various training options and parameters, such as the frequency of checkpoints, are customizable.

Testing and Visualization

To verify model performance, the testing phase involves:

aocr test ./datasets/testing.tfrecords

For a detailed understanding, users can visualize the attention mechanisms employed by the model during its evaluations:

aocr test --visualize ./datasets/testing.tfrecords

Exporting the Model

Upon attaining satisfactory results, the model can be exported for future use in formats like a SavedModel or a frozen graph:

# Default SavedModel export
aocr export ./exported-model

# Export as a frozen graph
aocr export --format=frozengraph ./exported-model

Serving the Model

The exported models can be served as a REST API, allowing for interaction over the web. This is facilitated via TensorFlow Serving, by launching:

tensorflow_model_server --port=9000 --rest_api_port=9001 --model_name=yourmodelname --model_base_path=./exported-model

Subsequent requests, such as predictions, can be performed using encoded binary inputs.

Google Cloud ML Engine Integration

This project can also be extended to the cloud, utilizing Google Cloud Machine Learning Engine for scalable training and deployment.

1. Define necessary environment variables.
2. Upload datasets to Google Cloud Storage.
3. Submit training jobs via the gcloud command.

Customization and Parameters

The project supports extensive customization, allowing users to fine-tune parameters related to logging, testing, exporting, and training processes to better suit specific needs or improve performance.

Overall, the attention-based OCR project is a powerful tool for converting image data to textual information, providing sophisticated methods and robust customization options for researchers, developers, and technologists.