R omanized to Native M alayalam Script Transliteration Using an Encoder-Decoder Framework (2025)

Abstract: In this work, we present the development of a reverse transliteration model to convert romanized Malayalam to native script using an encoder-decoder framework built with attention-based bidirectional Long Short Term Memory (Bi-LSTM) architecture. To train the model, we have used curated and combined collection of 4.3 million transliteration pairs derived from publicly available Indic language translitertion datasets, Dakshina and Aksharantar. We evaluated the model on two different test dataset provided by IndoNLP-2025-Shared-Task that contain, (1) General typing patterns and (2) Adhoc typing patterns, respectively. On the Test Set-1, we obtained a character error rate (CER) of 7.42%. However upon Test Set-2, with adhoc typing patterns, where most vowel indicators are missing, our model gave a CER of 22.8%.

## introduction typing in native script has always remained a challenge for speakers of many indian languages including malayalam, across diverse digital platforms. in the pre-smartphone era, where native language typing was virtually non-existent due to the unavailability of accessible and user-friendly keyboards, typing malayalam in the roman script was the norm. even with advancements in technology, typing in the roman script has become the natural and preferred mode of input across devices for an average user @xcite . while romanized communication seems convenient, it is not preferred in formal contexts. transliteration from romanized input to native scripts is inherently complex due to variations in typing styles, the absence of standardized romanization schemes, and the context-dependent nature of character mappings. hence there is a need for real-time transliteration tools that can seamlessly convert romanized malayalam into its native script. in this work, we address this need by developing a robust reverse transliteration model for malayalam, where romanised malayalam is automatically converted into native script. the proposed model leverages an attentionbased bidirectional long short term memory (bi-lstm) encoder-decoder framework, trained on large-scale transliteration datasets, namely dakshina @xcite and aksharantar @xcite . the code for training the model is published under mit license foot_0 . this paper outlines the related works, datasets, model architecture and results, highlighting the model's performance on datasets that reflect both general and adhoc typing patterns. ## related works rule-based and data-driven approaches are the two main strategies for transliteration @xcite . prior to the advent of deep learning approaches of learning from huge data, rule based approaches were the norm. in the context of well defined romanization standards @xcite , rule based approaches are the best in terms of speed and accuracy. however there are non-standard romanised malayalam used in informal communication contexts, that calls for deep learning solutions. a rule based system available for transliteration among indian languages based on soundex algorithms is introduced in libindic @xcite . aksharamukha script converter is is another rule-based systems that transliterates among 121 scripts and 21 standard romanization methods @xcite . the brahmi-net tool covers 306 language pairs across 13 indo-aryan, 4 dravidian languages, and english, utilizing an unsupervised method to mine parallel transliteration corpora for statistical training. this hybrid system leverages unicode ranges and an extended itrans encoding to enable script conversions between brahmiderived scripts @xcite . deep learning approaches rely on carefully crafted transliteration corpora for training the models. dakshina is an open licensed and curated transliteration corpora @xcite consisting of native script text, a romanization lexicon and some romanized full sentences in 12 south asian languages. aksharantar is the largest publicly available transliteration dataset with 26 million transliteration pairs for indian languages created by mining from monolingual and parallel corpora, as well as collecting data from human annotators @xcite . it has also been reported that mined name pair datasets @xcite could be used for training general purpose transliteration models @xcite . a multitask learning based training for multilingual neural transliteration leveraging orthographic similarity between languages was described in @xcite . non-neural method like pair n-gram and nueral methods like sequencesequence lstm and transformer architectures were compared in roark et al. for single words transliteration task. transliteration implemented using neural machine translation system (nmt) was proposed by @xcite was used for training the model. indicxlit is a multilingual neural transliteration model trained on the aksharantar @xcite dataset using an encoder-decoder transformer architecture. grapheme to phoneme conversion systems for mapping of malayalam script to precise romanisation schemes have been explored in rule based @xcite @xcite and data driven @xcite fashions. transliterating sentences are considered as a different task than transliterating single words in @xcite . identifying word contexts can improve sentence level transliteration. kirov et al. describes methods to incorporate language models to improve transliteration of full sentences as opposed to single words. ## methodology the methodology involved in this study encompasses the curation and preprocessing of training datasets, design of the model architecture, training, and evaluation on the test data set. in the current work, we train word-level transliteration model. during testing, we preprocess sentences by extracting individual words, performing word-level transliteration, and then reconstructing the full sentence in the post-processing stage. nonalphabetic characters like punctuation and numbers are excluded from model input, preserved in their original positions, and reinserted after generating the transliterated token sequence. ## discussion in test set-1 with standard typing patterns, the model achieved a 7.4% cer, demonstrating strong performance aligned with the model's training data. for test set-2 involving adhoc typing patterns with frequent vowel omissions, the model's performance significantly declined as indicated by the performance metrics. while most test sentences exhibited low character-level error rates, the accompanying wer and bleu scores appear comparatively poor. this does not indicate model inadequacy, but rather reflect the inherent limitations of wer and bleu scores in evaluating sentence transliterations. as they penalize even minor character variations as complete word errors misrepresenting the transliteration quality @xcite ( ## conclusion our reverse transliteration model for malayalam demonstrates promising capabilities in converting romanized text to native script, particularly for standard typing patterns. however, the research reveals significant challenges in handling adhoc typing styles, especially those with frequent vowel omissions. future efforts should focus on fine-tuning the model using a diverse dataset that includes a significant proportion of adhoc typing patterns to enhance its robustness and adaptability.