A Review on Handwritten Malayalam to English Digitization and Translation

Abstract

This paper provides a thorough overview of recent developments in handwritten Malayalam text recognition (HCR) and Malayalam–English machine translation (MT), highlighting the shift from traditional, rule based systems to modern ap- proaches using deep learning and transformer models. The review covers a range of methodologies, including classical Optical Character Recognition (OCR) techniques, statistical machine translation (SMT), neural machine translation (NMT), and new Vision Language Models (VLMs). Earlier OCR systems that relied on manually extracted features achieved only moder- ate accuracy, but recent approaches using convolutional and residual neural networks have achieved over 99% accuracy on benchmarks like P-ARTS Kayyezhuthu. The use of transfer learning and hybrid CNN BiLSTM models has further improved performance, especially in challenging conditions. In the field of translation, hybrid SMT systems have improved grammatical accuracy through better morphological processing, while atten- tion based NMT and transformer models such as MarianMT, T5, and BART have significantly boosted BLEU scores and natural fluency. Newer frameworks like Nayana OCR combine OCR and translation using synthetic data that considers layout and LoRA based fine tuning, making the systems more scalable for less resourced scripts. Although there have been significant advancements, several challenges remain, such as the lack of large datasets, limited integration between OCR and MT systems, and the complex morphology of the Malayalam language. The paper ends by suggesting future research directions, including the development of unified OCR translation systems, synthetic data creation, and multimodal pre-training, all aimed at creating more effective and scalable Malayalam–English handwritten text translation systems.

Introduction

The digitization and translation of handwritten Malayalam text into English is a challenging research area that combines computer vision, pattern recognition, and natural language processing. Malayalam’s complex script structure—featuring ligatures, diacritics, and non-linear glyphs—makes both optical character recognition (OCR) and machine translation (MT) more difficult than for alphabetic languages. Early OCR systems based on handcrafted features and classical classifiers achieved only moderate accuracy and struggled with writing variations and noise.

The field has significantly advanced with the adoption of deep learning. Convolutional Neural Networks (CNNs), ResNets, and hybrid CNN–BiLSTM models have largely replaced manual feature engineering and now achieve near-human accuracy, often exceeding 99% on benchmark datasets. Transfer learning and synthetic data generation have helped address Malayalam’s low-resource nature, enabling strong performance even with limited datasets.

Malayalam–English machine translation has similarly evolved from rule-based and statistical models to neural and transformer-based approaches. Modern transformer models such as MarianMT, T5, and BART provide substantial improvements in translation quality, achieving higher BLEU and ROUGE scores through attention mechanisms, subword tokenization, and transfer learning. Recent Vision–Language Models (VLMs) and multimodal frameworks, such as Nayana OCR, integrate OCR and translation into unified pipelines using techniques like LoRA fine-tuning and synthetic data.

Despite these advances, challenges remain, including segmentation errors, data scarcity, inconsistent evaluation standards, and limited end-to-end OCR–MT integration. The review of studies from 2010 to 2025 highlights a clear shift toward deep neural and transformer-based models and identifies future research directions such as cross-modal fine-tuning, morphology-aware tokenization, standardized benchmarks, and shared multilingual datasets. These efforts are essential for building scalable, accurate, and robust end-to-end Malayalam handwritten text translation systems.

Conclusion

Over the past decade, research on Malayalam Optical Character Recognition (OCR) and language translation has progressed from handcrafted systems to advanced deep learn- ing frameworks. Early OCR models that depended on manually designed structural and statistical features [1], [4] achieved only moderate performance, particularly when handling complex ligatures and compound characters. The ad- vent of convolutional neural networks (CNNs) and residual networks (ResNets) [10], [12], [16] introduced robust hierarchical feature extraction, greatly improving character level accuracy and generalization. Ensemble approaches, including CNN–BiLSTM hybrids [8], and transfer learning based ResNet50 systems [22] further boosted recognition performance on datasets such as Amrita MalCharDb and P-ARTS Kayyezhuthu. In parallel, translation technology evolved from rule based and statistical paradigms [25], [30] to neural and transformer based systems [13], [14], [26], [27]. Modern models like Mar- ianMT and T5 employ attention mechanisms and multilingual transfer learning to generate context aware and grammatically accurate Malayalam–English translations. Recent multimodal frameworks such as Nayana OCR [23] mark a shift toward unified vision language pipelines by integrating OCR and machine translation in a single end to end system, leveraging synthetic and layout aware training data. Despite these advances, several challenges persist, including limited availability of annotated corpora [7], [8], [10], incon- sistent evaluation protocols [19], [20], and the lack of adaptation to specialized domains like legal or administrative docu- ments [21]. Future research must emphasize building large, standardized datasets, developing reproducible benchmarks, and employing synthetic data augmentation [23] to compensate for resource scarcity. Moreover, approaches that focus on cross modal pre training and morphology aware tokenization [12], [13] are expected to improve alignment between visual and linguistic representations in Malayalam. With continued focus on data scalability, transformer optimization, and domain adaptation, the development of a fully automatic, linguistically robust Malayalam–English handwritten text translation system appears increasingly attainable. ‘

References

Over the past decade, research on Malayalam Optical Character Recognition (OCR) and language translation has progressed from handcrafted systems to advanced deep learn- ing frameworks. Early OCR models that depended on manually designed structural and statistical features [1], [4] achieved only moderate performance, particularly when handling complex ligatures and compound characters. The ad- vent of convolutional neural networks (CNNs) and residual networks (ResNets) [10], [12], [16] introduced robust hierarchical feature extraction, greatly improving character level accuracy and generalization. Ensemble approaches, including CNN–BiLSTM hybrids [8], and transfer learning based ResNet50 systems [22] further boosted recognition performance on datasets such as Amrita MalCharDb and P-ARTS Kayyezhuthu. In parallel, translation technology evolved from rule based and statistical paradigms [25], [30] to neural and transformer based systems [13], [14], [26], [27]. Modern models like Mar- ianMT and T5 employ attention mechanisms and multilingual transfer learning to generate context aware and grammatically accurate Malayalam–English translations. Recent multimodal frameworks such as Nayana OCR [23] mark a shift toward unified vision language pipelines by integrating OCR and machine translation in a single end to end system, leveraging synthetic and layout aware training data. Despite these advances, several challenges persist, including limited availability of annotated corpora [7], [8], [10], incon- sistent evaluation protocols [19], [20], and the lack of adaptation to specialized domains like legal or administrative docu- ments [21]. Future research must emphasize building large, standardized datasets, developing reproducible benchmarks, and employing synthetic data augmentation [23] to compensate for resource scarcity. Moreover, approaches that focus on cross modal pre training and morphology aware tokenization [12], [13] are expected to improve alignment between visual and linguistic representations in Malayalam. With continued focus on data scalability, transformer optimization, and domain adaptation, the development of a fully automatic, linguistically robust Malayalam–English handwritten text translation system appears increasingly attainable. ‘

Copyright

Copyright © 2025 Mohammed Farhan, Mohammed Nowfal, Radhesyam Raghav, Sabah K J, Sneha Haridas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.