---
title: 'IsharaKotha: Bangla Sign Language Corpus'
url: https://www.emergentmind.com/topics/isharakotha
type: topic
---

# IsharaKotha: Bangla Sign Language Corpus

IsharaKotha is an avatar-based Bangla Sign Language (BSL) resource designed for text-to-sign translation, integrating a structured linguistic corpus encoded in the Hamburg Notation System (HamNoSys) with a modular animation rendering pipeline. It is the first comprehensive, HamNoSys-based Bangla Sign Language corpus and supports both research and practical applications requiring dynamic sign generation and avatar animation from Bangla text input [2511.16896].

## 1. Corpus Architecture and Linguistic Representation

The IsharaKotha corpus is phonetically encoded using HamNoSys, a notation system developed for detailed, language-agnostic transcription of signed languages. Each sign, corresponding to a letter, digit, or word, is decomposed into five features:

- Handshape (~200 possible configurations)
- Orientation (six principal palm/finger directions)
- Location (over 30 body-relative positions)
- Movement (e.g., straight, circular, repeated)
- Non-manual features (facial expressions, head/lip movement)

For instance, the sign for the Bangla word for "book" combines a two-hand symmetry operator, flat handshapes with palmar orientation inward, finger contact at chest height, an opening motion, and no non-manual markers. Signs authored in HamNoSys are converted into SiGML (Signing Gesture Markup Language) XML files using the SiS-Builder toolkit, with separate `<hamnosys_manual>` and `<hamnosys_nonmanual>` tags representing manual and non-manual components, respectively, and a `gloss` attribute linking each sign to its Bangla term [2511.16896].

## 2. Corpus Scope, Coverage, and Metadata

The corpus comprises 3,823 annotated sign entries, spanning alphabets, digits, and 34 semantic classes of vocabulary:

| Category                   | Entries |
|----------------------------|:-------:|
| Alphabets                  |   49    |
| Digits                     |   10    |
| Word signs (34 classes)    | 3,764   |
| **Total**                  | **3,823**   |

Primary semantic domains include Crime & Law (38), Economics (35), Food & Drinks (234), Household Items (342), Human Characteristics (470), Sports (53), and Others (776). Each entry is annotated with:

- Bangla orthographic gloss
- HamNoSys transcription
- SiGML file (manual/non-manual)
- Semantic category tag

This structure enables downstream NLP tasks, comprehensive annotation, and consistent mapping between Bangla text and sign form [2511.16896].

## 3. Text-to-Sign Translation Pipeline and Lemmatization

The IsharaKotha workflow operates as follows:

1. **Input Processing:** Raw Bangla sentences are segmented and tokenized.
2. **Lemmatization:** Inflected tokens are mapped to lemmas using a deep learning–based sequence-to-sequence (Seq2Seq) model with global attention. The architecture uses a Bi-LSTM encoder to process input character sequences, an attention function:
   \[
   \alpha_{t,s} = \frac{\exp(e_{t,s})}{\sum_{s'}\exp(e_{t,s'})}
   \]
   where \( e_{t,s} = v^\top \tanh(W_s h_s + W_d s_{t-1}) \), a unidirectional LSTM decoder, and a softmax output layer:
   \[
   \mathcal{L}_{CE} = -\sum_{t=1}^{T}\sum_{v=1}^{|V|} y_{t,v}\log\hat{y}_{t,v}
   \]
   Trained on a corpus of 94,781 word-form pairs, the lemmatizer achieves 79.22% accuracy.
3. **SiGML Retrieval:** For each lemma, a pre-computed SiGML file is located.
4. **Animation Rendering:** SiGML sequences are rendered via a 3D avatar engine [2511.16896].

## 4. Avatar-Based Animation Generation

The rendering engine uses the JASigning platform to translate SiGML into avatar motion, mapping HamNoSys features as follows:

- Handshapes: Symbol-to-joint angle presets
- Orientation/Location: Relative palm/finger orientation mapped to avatar body coordinates
- Movement: Bézier-style interpolation of hand trajectories
- Non-manual features: Head and facial animations parsed from `<hamnosys_nonmanual>` tags

This pipeline supports real-time signing at ≈30 fps, enabling dynamic generation without recourse to pre-recorded video data [2511.16896].

## 5. Evaluation Protocol and Quantitative Results

Evaluation was performed via a publicly accessible web interface, partitioned into alphabets, digits, word categories, and sentences. Three evaluators (two professional sign interpreters and one hearing-impaired athlete) rated each animation on a scale (“Bad”=1, “Average”=2, “Good”=3, “Excellent”=4), yielding 3,828 ratings overall.

Distribution of ratings:

| Rating     | Count | Percentage |
|------------|-------|------------|
| Bad        | 116   | 3.03%      |
| Average    | 294   | 7.68%      |
| Good       | 2,346 | 61.29%     |
| Excellent  | 1,072 | 28.00%     |

The aggregate mean score is \( \bar{x} = 3.14 \), with a variance of ≈0.4774, standard deviation ≈0.691, and a 95% confidence interval of \( 3.14\pm0.02 \). Digits scored ≈3.6–4.0, while full sentence signing scored ≈3.06–3.32, reflecting relatively higher rates of lemmatizer errors (≈20% inflection mistransformations) and incomplete lemma coverage [2511.16896].

## 6. Applications, Limitations, and Future Directions

IsharaKotha supports applications including e-learning for the hearing-impaired, real-time smartphone/web text-to-sign translation, and sign language production or annotation for NLP research. Notable limitations include an estimated 1% omission rate for semantic units (chiefly directional/facial signs), reliance on a static dictionary with incomplete full-sentence coverage, and simplified avatar facial expression modeling. Extensions under development involve expanding the sign inventory (>10,000 entries), improving morphological analysis (target >90% lemmatizer accuracy), refining avatar blendshapes and eye gaze, grammar-based reordering for smoother multiword signing, and computer vision–assisted semi-automated HamNoSys transcription from usable video corpora [2511.16896].

By providing a rigorously annotated, extensible, and openly accessible Bangla Sign Language resource based on HamNoSys and SiGML standards, IsharaKotha establishes the foundation for scalable, dynamic text-to-sign translation systems and advances the state of computational sign linguistics for Bangla.

Source: https://www.emergentmind.com/topics/isharakotha