Language Technologies for Low-Resourced Languages

Developing native natural language foundations, high-precision document recognition, and speech intelligence tailored specifically for underserved languages, right-to-left scripts, and regional dialects.

The Core Challenge

Mainstream AI models systematically underperform on languages with limited digital footprints. SoftMax R&D addresses this gap directly through curated Unicode corpora, custom morphological tokenization, right-to-left optical extraction, and acoustic phoneme mapping.

Morphological Analysis Token Efficiency Vision Transformers Acoustic Phonetics Unicode Normalization
Core Capabilities

Large Language Models (LLM)

Foundation models, custom tokenizers, and instruction adaptation built to handle the complex grammar, inflection, and morphology of low-resourced languages.

  • Subword & morphological tokenizers
  • Instruction fine-tuning & domain adaptation
  • Retrieval-Augmented Generation (RAG)
  • Evaluation benchmarks for Kurdish dialects

Optical Character Recognition (OCR)

High-fidelity document transcription tailored for complex Arabic-script cursive writing, multi-column print, and historical Kurdish manuscripts.

  • Complex Right-to-Left (RTL) cursive recognition
  • Document layout & table segmentation
  • Historical archive and book digitization
  • Automated character correction & Unicode normalization

Speech AI: ASR & TTS

End-to-end speech technologies converting voice to text and text to authentic regional voice synthesis under low-data training regimes.

  • Automatic Speech Recognition (ASR) for regional dialects
  • Neural Text-to-Speech (TTS) voice generation
  • Grapheme-to-Phoneme (G2P) phonetic mapping
  • Low-latency streaming models for live applications