Deep Learning Based Approach for Handwritten Text Recognition from Multimedia Documents

This project focuses on developing a deep learning model for recognizing handwritten text within multimedia documents. The approach leverages advanced neural network architectures and the Beam Search Algorithm to enhance the accuracy and efficiency of text recognition from diverse document types.

Features

• Handwritten Text Recognition: Capable of accurately recognizing and transcribing handwritten text from multimedia documents.
• Deep Learning Models: Utilizes state-of-the-art neural network architectures for feature extraction and text recognition.
• Beam Search Algorithm: Implements the Beam Search Algorithm to improve the accuracy of text decoding by exploring multiple possible sequences.
• Multimedia Document Processing: Designed to handle various types of multimedia documents including scanned images and PDFs.

Tools and Technologies

• Python: Programming language used for model implementation and data processing.
• TensorFlow/Keras: Libraries used for building and training deep learning models.
• OpenCV: Utilized for image preprocessing and manipulation.
• NumPy: Supports numerical operations and data handling.
• Pandas: Used for data manipulation and preprocessing.
• Matplotlib/Seaborn: Employed for data visualization and model performance evaluation.

Beam Search Algorithm

Beam Search Algorithm

The Beam Search Algorithm is a heuristic search algorithm that explores a graph by expanding the most promising nodes within a limited set. It is particularly useful in sequence decoding problems, such as handwriting recognition or machine translation, where the goal is to find the most likely sequence of words or characters from a model's predictions.

How It Works

1. Initialization:

   • Start with the initial node (e.g., the beginning of the sequence) and initialize a beam width (k), which represents the number of top sequences to keep at each step.

2. Expansion:

   • At each step, expand all possible sequences from the current top sequences. Each possible next token or character is added to each of the current sequences to form new sequences.

3. Scoring:

   • Score the newly formed sequences based on their probability. This is usually done by the model's output probabilities.

4. Selection:

   • Select the top k sequences based on their scores and discard the rest. These top sequences are then used for the next step of expansion.

5. Termination:

   • Repeat the expansion, scoring, and selection steps until the end of the sequence is reached or a predefined stopping criterion is met.

Visualization

Here's a visual representation of the Beam Search Algorithm:

To know more about the algorithm: Wikipedia

Advantages

• Efficiency: Reduces the number of sequences explored compared to exhaustive search methods, making it feasible for long sequences.
• Flexibility: The beam width can be adjusted to balance between computational complexity and accuracy.

Disadvantages

• Beam Width Limitation: A smaller beam width might miss the optimal sequence, while a larger width increases computation.
• Greedy Approach: Beam Search is heuristic and might not always find the optimal solution.

Dataset

The dataset used for training and evaluating the model consists of handwritten text samples from various multimedia documents.

How to Run

1. Clone the Repository:

   git clone https://github.com/SaiVishwa021/Handwritting_recognition_for_documents.git

Acknowledgements

1. TensorFlow/Keras for providing powerful deep learning frameworks.
2. OpenCV for robust image processing tools.
3. The research community for the foundational work in handwriting recognition and Beam Search Algorithms.