Transformer-based Optical Text Recognition for Low-Resource Languages: A Case Study on Bengali and Nepali

Core Concepts

This study proposes a transformer-based model for accurate optical text recognition in Bengali and Nepali, two low-resource languages with unique script characteristics. The model achieves low character and word error rates, demonstrating its potential for practical applications like document digitization and text extraction.

Abstract

The paper discusses the development and evaluation of a transformer-based optical character recognition (OCR) model for Bengali and Nepali, two low-resource languages in South Asia.
Key highlights:

Unique characteristics of Bengali and Nepali scripts, such as dependent letters, overlapping characters, word separation, and confusing shapes, make OCR a challenging task.
The proposed model uses a transformer-based approach, leveraging the encoder-decoder architecture of the TrOCR model.
The encoder, a Vision Transformer (ViT), extracts features from the input images, while the decoder, a multilingual RoBERTa model, generates the recognized text.
The model was trained on the BanglaWriting dataset for Bengali and a manually curated dataset for Nepali.
During training, the model achieved low Word Error Rates (WER) of 0.10 and 0.14, and Character Error Rates (CER) of 0.04 and 0.10, for Bengali and Nepali, respectively.
On the test set, the model maintained a strong performance, with average CER and WER of 0.07 and 0.12 for Bengali, and 0.11 and 0.15 for Nepali.
The results demonstrate the model's potential for practical applications in text extraction and document digitization for these low-resource languages.

Stats

The model was trained on the BanglaWriting dataset, which contains 21,234 words and 32,784 characters, and a manually curated dataset for Nepali with around 7,000 words and 10,000 characters.

Quotes

"The outcomes demonstrate that the suggested technique achieves high precision in deciphering typed text in Bengali and Nepali."
"This study could aid in the advancement of linguistic technology in South Asia and be useful in a number of industries, including automation, administration, and education."

Key Insights Distilled From

Optical Text Recognition in Nepali and Bengali

by S M Rakib Ha... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02375.pdf

Deeper Inquiries

How can the proposed transformer-based approach be extended to other low-resource languages with unique script characteristics?

The transformer-based approach proposed in the study can be extended to other low-resource languages with unique script characteristics by following a few key steps. Firstly, it is essential to gather a sufficient amount of training data for the target language, even if it is limited. This data should include a diverse range of text samples, both handwritten and printed, to ensure the model learns the nuances of the script. Additionally, pre-processing techniques such as image resizing, denoising, and normalization should be adapted to suit the specific characteristics of the new language's script.
Furthermore, the feature extraction encoder and language modeling components of the transformer model can be fine-tuned to accommodate the distinct features of the new language. This may involve adjusting the patch size, embedding layers, or positional encodings to better capture the unique characteristics of the script. By training the model on a dataset specific to the new language and fine-tuning the architecture to account for its script characteristics, the transformer-based approach can be effectively extended to other low-resource languages.

What are the potential challenges in deploying this OCR model in real-world applications, and how can they be addressed?

Deploying the OCR model based on transformer architecture in real-world applications may pose several challenges that need to be addressed for successful implementation. One significant challenge is the variability in image quality and font styles encountered in practical scenarios. Images with complex backgrounds, unusual fonts, or low resolution can impact the model's performance and accuracy. To address this challenge, the model can be further trained on a diverse set of images that mimic real-world conditions, including different font styles, sizes, and orientations.
Another challenge is the computational resources required to run the model efficiently, especially in real-time applications. Optimizing the model for inference speed and memory usage can help mitigate this challenge. Techniques such as quantization, model pruning, and hardware acceleration can be employed to streamline the model's deployment and improve its efficiency in real-world applications.
Additionally, ensuring the model's robustness to noise, distortion, and variations in text alignment is crucial for its reliability in practical settings. Data augmentation techniques, robust training strategies, and post-processing methods can be implemented to enhance the model's resilience to such challenges and improve its overall performance in real-world OCR applications.

How can the model's performance be further improved by incorporating additional linguistic features or leveraging multilingual language models?

To enhance the model's performance, incorporating additional linguistic features specific to the target languages can be beneficial. This may include integrating language-specific dictionaries, grammar rules, or phonetic information into the model to improve its understanding of the language structure and context. By incorporating linguistic features, the model can make more informed predictions and achieve higher accuracy in text recognition tasks.
Furthermore, leveraging multilingual language models, such as XLM-RoBERTa, can also boost the model's performance across multiple languages. These models are pre-trained on a diverse range of languages and can capture cross-lingual patterns and representations effectively. By fine-tuning the model on multilingual data, the OCR model can benefit from the shared knowledge and linguistic representations, leading to improved performance in recognizing text from various languages.
Incorporating domain-specific knowledge, such as language-specific character embeddings or contextual information, can further enhance the model's ability to handle complex scripts and linguistic nuances. By combining additional linguistic features with multilingual language models, the OCR model can achieve higher accuracy, robustness, and adaptability across a wide range of languages and scripts.