Bengali Handwritten Text Generation

• Bengali handwritten text generation is the algorithmic synthesis of handwritten images that capture both linguistic content and diverse writing styles using methods such as GANs and deterministic deformations.
• Researchers employ advanced neural architectures, including conditional VAE–GANs and character-mask generators, to disentangle content and style while ensuring digitization fidelity.
• Data augmentation techniques and carefully curated datasets, like BanglaWriting and BeHGAN, lead to measurable improvements in recognition accuracy and support broader applications in document digitization and forensics.

Bengali handwritten text generation refers to the algorithmic synthesis of handwritten Bengali word images that capture both the linguistic content and the stylistic diversity of real handwriting samples. This field is motivated by the scarcity of large, representative annotated datasets for Bengali, which is the fifth most spoken language globally, and the demand for synthetic data to advance recognition systems, document digitization, and scriptual forensics. Bengali handwritten text generation employs a range of methods, from deterministic font-based deformations to advanced conditional generative adversarial networks (GANs) and cross-modal neural architectures, and increasingly leverages writer-aware disentangled representations and adversarial training to model both lexical content and stylistic variation (Mridha et al., 2020, Islam et al., 25 Dec 2025, Roy et al., 2018, Akter et al., 2023).

1. Datasets for Bengali Handwritten Text Generation

The synthesis of realistic Bengali handwriting relies on meticulously curated datasets that reflect the natural variability of the script.

BanglaWriting Dataset: This dataset features 21,234 word images and 32,787 characters, produced by 260 individuals of ages 8–60 years (mean ≈ 24 years), with 40% female and 60% male participants spanning 8 districts. Each single-page handwriting sample includes word-level bounding boxes and unicode transcriptions. Unique features include 5,470 distinct Bengali words, as well as 261 overwriting and 450 strike-through samples, all with manually generated bounding-boxes and labels. The provided script (banglawriting_script2020) corrects lighting, removes background noise, and standardizes to grayscale or binary format. The dataset enables writer-disjoint splitting, facilitating robust generalization evaluation (Mridha et al., 2020).

BeHGAN Dataset: Compiled via both online and offline recruitment, this resource contains annotated samples from approximately 500 writers covering a demographically balanced pool. Each contributor wrote 30 words (single-, two-, and three-character combinations) on standardized sheets. After quality filtering and augmentation, final image counts are 8,600 (single char), 19,400 (two-char), and 12,000 (three-char). Images are tightly cropped, resized to category-dependent shapes (16×32 to 48×32 pixels), and provided as 8-bit grayscale (Islam et al., 25 Dec 2025).

Bangla.AI Handwritten Grapheme Dataset: Used in character-to-word synthesis pipelines, this set contains 10,000 samples capturing 49 base letters and 18 diacritics. Each grapheme is stored as a grayscale PNG, with tight-bounding box cropping and normalization (Akter et al., 2023).

Synthetic Data Resources: Digital corpora (e.g., Emille, Indic NLP, news sites) are converted to word bitmaps using diverse Bangla fonts and augmented with distortion operators to mimic writer variability. Synthetic sets can exceed 24,000 word images with parameterized diversity (Roy et al., 2018).

2. Model Architectures and Content–Style Disentanglement

State-of-the-art architectures for handwritten Bengali word generation incorporate conditionality on both unicode content and independently sampled style vectors, often using advanced neural generative frameworks.

Conditional VAE–GAN for BanglaWriting: The model comprises:

• A content encoder $E_c(x, u)$, producing parameters $(\mu^c, \sigma^c)$ for the unicode string $u$;
• A style encoder $E_s(x)$ extracting $(\mu^s, \sigma^s)$ representing handwriting style factors (writer ID, pen, slant);
• Independent sampling of $z^c$ (content) and $z^s$ (style) from their posterior distributions;
• A generator $G(z^c, z^s)$ combining these codes via concatenation or FiLM-style fusion to yield a synthetic image $\hat{x}$;
• A discriminator $D(x)$ for adversarial training.

Losses include adversarial, reconstruction ($L_1$ pixel), KL-divergence regularizers for both style and content posteriors, and RNN-based cross-entropy to align $z^c$ with target unicode strings. These components allow for style-invariant content synthesis or controlled style transfer (Mridha et al., 2020).

BeHGAN Pipeline: Building on the “ScrabbleGAN” semi-supervised paradigm:

• The generator $G$ takes as input an embedding of target text (per-character one-hots) and a global noise/style vector $Z \sim \mathcal{N}(0, I)$.
• Cascaded “character-mask” generators independently synthesize spatial patches, with architectural overlap in receptive fields to enforce continuous handwriting across letters.
• Style vector $Z$ modulates pen thickness, slant, and spacing at intermediate layers.
• The discriminator $D$ operates patch-wise, using spectral normalization for training stability.
• An auxiliary CRNN recognizer $R$ imposes content regularization via CTC loss to enhance cross-modal fidelity (Islam et al., 25 Dec 2025).

Deterministic Grapheme Concatenation: An alternative (lower-complexity) composition approach generates synthetic words by horizontally stitching preprocessed character PNGs. Overlap parameters (e.g., 0 or 4 pixels) simulate explicit or cursive character joining. Optional blending reduces inter-glyph discontinuities (Akter et al., 2023).

3. Data Augmentation and Synthetic Text Generation Techniques

Synthetic data amplification is essential to address dataset sparsity and to populate rare wordforms.

Parameterized Font Deformation: Techniques include:

• Vector-based random shifts and Gaussian-perturbations to skeletal control points;
• Raster-level warps: curved/rainbow, sinusoidal, and elliptical bulges, each modulated by amplitude and frequency parameters;
• Multiple transforms are composed for maximal diversity, maintaining legibility through bounded parameter ranges (≤10–15% of image size).

Combined synthetic and real–handwriting training consistently yields a 5–7% recognition performance gain (e.g., word recognition accuracy on combined sets: 77.6% vs. 74.6% with synthetic only) (Roy et al., 2018).

Data Augmentation in GAN Pipelines: Both BanglaWriting and BeHGAN employ random scaling, affine/projection transforms, Perlin-noise stroke jitter, elastic distortions, and color/contrast jitter on real and synthetic word images (Mridha et al., 2020, Islam et al., 25 Dec 2025).

Grapheme-Word Composition: In pipelines constructing word images from component graphemes:

• Multi-character words are synthesized by placing tight-cropped glyphs according to layout constraints, varying their overlap and alignment.
• On-the-fly augmentations include small rotations, scale jitter, elastic deformations, and noise injection to narrow the domain gap (Akter et al., 2023).

Augmentation Technique	Domain	Parameter Example
Curved/rainbow warping	Raster	Amplitude ≈ 0.15·h
Gaussian-perturbation	Vector	σ = 0.02·min(w,h)
Stroke jitter (Perlin)	GAN	Network-internal noise, N/A
Affine/projection	GAN/Raster	±5° rotation, ±10% scaling

4. Training Protocols and Evaluation Metrics

Models are trained on large-scale, writer-diverse sets with stringent validation and objective measures.

Training Regimes:

• Adam optimizer with $\beta_1=0.5,\,\beta_2=0.999$ and learning rates in $[2\times10^{-4}, 1\times10^{-3}]$;
• Batch sizes typically 32–64 images, epoch range 50–300;
• Writer-disjoint splits are used to assess generalization to unseen handwriting (Mridha et al., 2020, Islam et al., 25 Dec 2025, Akter et al., 2023).

Quantitative Evaluation:

• Structural Similarity Index Measure (SSIM), Fréchet Inception Distance (FID), and Geometric Score (GS) are employed for synthetic–real image similarity.
• Best reported results (BeHGAN) are SSIM = 0.67, FID = 41, GS = 0.63 after augmentation and post-processing enhancements (Islam et al., 25 Dec 2025).
• For synthetic–real recognition boosts: Top-1 word accuracy reaches 77.6% (combined synthetic + real, HMM), and 92% CNN+BiLSTM+CTC accuracy on non-overlapped synthetic sets (Roy et al., 2018, Akter et al., 2023).

Content Fidelity Metrics:

• OCR recognition accuracy via string edit distance to the ground-truth unicode sequence.
• Recognition auxiliary losses (CTC) to jointly optimize for legibility and realism (Mridha et al., 2020, Islam et al., 25 Dec 2025).

Style Metrics:

• Writer-similarity via cosine similarity or AUC on style embeddings of real vs. generated samples.
• Qualitative evaluation protocols include style interpolation and side-by-side grids for native speaker Turing-tests (Mridha et al., 2020).

5. Limitations and Future Research Directions

Current Bengali handwritten text generators face several substantive challenges:

• Restricted Vocabulary and Alphabet: Many GAN-based generators only operate on a small alphabet subset (e.g., five base characters plus combinations), with no support for conjunct consonants or full 50-letter coverage. Word lengths are generally restricted (max three characters) due to architectural constraints (Islam et al., 25 Dec 2025).
• Resolution Constraints: Output images remain low (16×32 to 48×32 px), imposing a bottleneck for downstream handwritten text recognition (HTR) model development (Islam et al., 25 Dec 2025).
• Style Variation and Adaptation: Existing models often fail to interpolate style codes or provide few-shot writer adaptation (i.e., capturing a new writer’s style from limited samples). No explicit end-to-end Bengali script recognizer is yet used for adversarial feedback (Islam et al., 25 Dec 2025).
• Synthetic–Real Domain Shift: Concatenative and deformative approaches may suffer from domain mismatch, especially if blending/overlapping is not tuned. Excessive overlap leads to unreadable images (Akter et al., 2023).

Anticipated Advancements:

• Expansion to full alphabet, coverage of conjuncts, and open-vocabulary/sentence-level generation.
• Integration of advanced generative models (e.g., StyleGAN3, diffusion models) for higher-resolution and sharper control over both global and local style.
• End-to-end architectures mapping Bengali Unicode directly to image, bypassing English proxies.
• Explicit style interpolation and writer disentanglement modules to enable robust style transfer and representational diversity (Islam et al., 25 Dec 2025).

6. Applications and Impact in Bengali Language Technologies

Bengali handwritten text generation has established new paradigms for:

• Augmenting limited natural handwriting datasets, thereby improving the recognition accuracy of HTR systems by 5–7% on numerals and characters and boosting word-level accuracy (combined synthetic + real, Top-1: 77.6%) (Roy et al., 2018).
• Driving writer identification, word segmentation, and cross-modal forensics.
• Enabling targeted research on age- and gender-specific handwriting variation, facilitated by datasets with annotated demographic metadata (Mridha et al., 2020).
• Supporting document digitization and archival of Bengali script material.
• Laying methodological foundations for cross-lingual or resource-scarce script synthesis via transferable architectures and principled data augmentation.

This suggests that further strengthening of synthetic generation techniques can directly raise the level of Bengali document understanding systems across the recognition, verification, and digital curation spectrum.