Advancements in Bangla Document Layout Analysis

Understanding digital documents can be quite challenging, especially when these documents are historical or written in different languages. One way to make this process easier is through Document Layout Analysis (DLA). DLA breaks down a document into parts, such as paragraphs, images, and tables. This separation helps machines accurately read and interpret the content of the documents.

In a recent competition, we focused on analyzing Bangla documents. We used a large dataset known as BaDLAD, which is filled with examples of various kinds of Bangla documents. Our main goal was to train a model called Mask R-CNN to assist in this analysis. After refining our model through careful adjustments, we achieved a good performance score of 0.889. However, we encountered challenges when we attempted to use a model designed for English documents, which did not work well with Bangla. This experience highlighted the unique difficulties associated with different languages.

#Document Layout Analysis

DLA is an important first step in digitizing documents. It sorts the elements of a document into recognizable sections, which is essential for Optical Character Recognition (OCR). OCR technology can then read the separated sections to extract text accurately. This process is particularly important for converting old or poorly maintained documents into formats that can be easily read by machines.

By analyzing the layout of a document, DLA allows the OCR engine to work more efficiently. It helps in identifying where the text is located and how to handle other elements like images and tables. This is especially relevant for historical documents, where formatting may be less standard than in modern texts.

#The Competition

The competition we participated in challenged us to create a DLA system specifically for Bangla documents. The BaDLAD dataset we used includes 33,695 documents that were carefully annotated by humans. The documents cover various categories, such as books, government documents, newspapers, and historical materials. This wide range of sources provided a robust base for training our model.

To tackle the challenge, we employed the Mask R-CNN model, which is well-known for segmenting objects in images. By fine-tuning this model on our dataset, we aimed to achieve a high level of accuracy in identifying the different sections of the documents. We also adjusted various settings, known as Hyperparameters, to enhance the model's performance.

#Model Training

Training a model like Mask R-CNN involves several steps. Initially, we started with a basic model that had not been trained before. This allowed us to see how well it could perform on our specific task. Although the initial results showed promise, we quickly realized that we needed to make adjustments to achieve better performance.

Using pre-trained weights from models designed for English text did not yield the results we had hoped for. This indicated that the challenges posed by Bangla text required a different approach. We continued to make changes, adjusting settings like learning rates-this determines how quickly the model learns from the data, and the number of training iterations, which is how many times the model goes through the dataset.

After several rounds of training with various hyperparameters, we noticed improvements. Starting with a learning rate of 0.007 and running a total of 22,000 iterations, we achieved a score of 0.88223. We then lowered the learning rate to 0.001 in further training sessions, which yielded better results. Each adjustment brought us closer to our goal.

#Fine-Tuning the Hyperparameters

Fine-tuning hyperparameters is critical in machine learning. For our project, we focused on several key parameters, including the base learning rate, the maximum number of training iterations, and the warmup iterations. Adjusting these parameters allowed us to improve the efficiency and effectiveness of our model.

As we continued to train, we experimented with different settings. As the learning rate became smaller, we found that the model's performance stabilized. We also reduced the number of warmup iterations so that the model's learning rate would not increase too quickly at the beginning of training.

The training effort spanned several submission sequences. With each sequence, we adjusted the parameters based on the results we observed. The goal was to find the right combination that would yield the highest score.

#Results Overview

By the end of our training process, which included 115,000 iterations in total, we achieved a final score of 0.889. This score indicates a high level of accuracy in segmenting the document layout. Our training approach showed that with careful adjustments and increased iterations, we could significantly enhance the performance of our model.

The results confirmed that maintaining an optimal learning rate and fine-tuning hyperparameters are crucial components in training machine learning models effectively. As we increased the dataset size, we also noticed improved model performance.

#Future Directions

Looking forward, we believe there is more work to be done. Our current model shows promise, but we aim to refine our approach even further. One area of focus will be enhancing the dataset to ensure it covers a broader range of document types and layouts. This may involve gathering more examples or expanding the existing dataset.

In addition, we plan to explore advanced techniques that could complement our current methods. Innovations in machine learning, such as different model architectures or additional training strategies, may provide further benefits.

As we continue to improve our DLA system for Bangla documents, we hope to contribute to advancements in fields like OCR, machine translation, and search. By developing better systems, we can make valuable resources more accessible to the millions of Bangla speakers.

#Conclusion

In summary, our work on Bangla document layout analysis shows that careful attention to hyperparameters and training processes can lead to significant improvements. We have demonstrated that using the Mask R-CNN model can yield effective results in understanding document layouts.

Challenges remain in further refining our approach and ensuring our model can adapt effectively to different languages and document types. Through ongoing efforts, we are excited about the potential to make digital documents more accessible for everyone.