About Mathla

Mathla is a UAE-based accessibility and artificial intelligence initiative focused on building practical technology for Arabic Sign Language and Deaf communication. Our work combines community-led language documentation, education, accessibility services, and AI research with one long-term goal: to enable natural communication between sign-language users and people who do not know sign language. We are building this from the ground up with Deaf users, reviewers, educators, researchers, families, institutions, and technology partners.

Mathla began from a real communication problem. In 2019, during a medical trip to Cairo with a Deaf family member, we encountered another Deaf person and expected sign language to make communication easy. It did not. The signs used across different Arab communities were not always the same, and there was no practical digital tool available to bridge the gap. That experience exposed a larger problem. Spoken languages already benefit from machine translation, speech recognition, text-to-speech, and increasingly powerful AI systems. Sign-language users do not yet have an equivalent communication layer that works naturally across everyday situations. Mathla was created to help close that gap.

Mathla was founded in the United Arab Emirates following a real communication experience in 2019 that highlighted the limitations faced by Deaf sign-language users across different Arabic-speaking communities. Today, Mathla is led from the UAE and brings together accessibility, software engineering, artificial intelligence, community participation, and sign-language research. Mathla is the public name of the platform and research initiative. Founder: Hamad Headquarters: United Arab Emirates Founded: 2019 Website: mathla.org Contact: [email protected]

Mathla is not one application. It is a connected platform built around three areas:

We are building a living Arabic Sign Language knowledge base through community contribution, Deaf-led validation, expert review, and regional awareness. The platform supports: • direct sign recording • human review • dictionary development • regional sign variation • campaigns • community contribution • learning resources This community layer is already live.

Mathla is developing services for organizations that need accessible communication and reliable sign-language resources. These include: • signed content production • custom dataset development • accessibility reviews • sign-language accuracy review • institutional campaigns • education and community projects

Mathla is also building the technology required for continuous sign-language understanding. The long-term AI objective is: natural signing video → visual understanding → sign / letter / number recognition → Arabic or English text → speech The reverse direction is also part of the roadmap: speech / text → signed communication The research is focused primarily on Arabic Sign Language, where high-quality data and mature AI systems remain limited.

Mathla has progressed beyond the concept stage into active product development, community operation, and AI research. Our current ecosystem includes: • A live public web platform • The Mathla iOS app, live on the App Store; Android in closed testing • Mobile sign-language recording and data collection • Community contribution workflows • Human review and validation systems • Arabic Sign Language dictionary infrastructure • Learning and academy components • Campaign and institutional participation tools • AI training and evaluation pipelines • Local and cloud-based model training infrastructure

One of the largest technical barriers in Arabic Sign Language AI is data. Mathla is investing heavily in building and organizing usable Arabic sign-language datasets. This includes: • community-recorded signs • reviewed dictionary material • structured mobile capture • broadcast and public video material • captioned and transcript-associated content • signer-aware evaluation data Mathla has collected and organized approximately 700 hours of Arabic sign-language-related video material for research and dataset development. We distinguish carefully between: Gold data — Human-reviewed and strongly aligned material suitable for trusted evaluation and supervised training. Weak or broadcast data — Larger-scale material that may still be useful for pretraining and representation learning, but is not automatically treated as ground truth.

Continuous sign-language translation is not the same as isolated sign recognition. Natural signing contains: • hand shape • hand orientation • body pose • facial expression • spatial relationships • movement • transitions • fingerspelling • numbers • regional variation • signer variation • variable timing Mathla therefore treats the problem as a temporal visual-language system. Current research includes structured visual features such as: • body pose landmarks • hand landmarks • facial landmarks • temporal motion • sequence modelling • transformer-based language models • Arabic text generation One of Mathla's current research pipelines converts signing video into structured body-pose, hand, and facial landmark sequences, which are then processed by temporal and transformer-based language models to investigate continuous signing-to-Arabic translation. This allows faster experimentation and clearer separation between visual understanding and language generation.

Our technical North Star is: Unsegmented continuous signing video → automatic temporal boundaries → SIGN / LETTER / NUMBER identities → ordered sequence → grounded Arabic or English → safe speech output This is significantly harder than recognizing isolated signs. The unresolved research challenges include: • automatic temporal segmentation • robust sign recognition • fingerspelling • signer generalization • Arabic language grounding • sequence reconstruction • safe uncertainty handling • real-time mobile inference

Mathla follows an evidence-first approach. A model is not considered successful simply because a demonstration looks good. We evaluate: • unseen signers • unseen sequences • temporal dependence • translation quality • false outputs • ghost outputs • safe silence • human-reviewed development sets • sealed test sets Failed experiments are recorded rather than hidden. For example, some early systems showed promising results but were later found to rely too heavily on sentence overlap or memorization. Those findings were used to strengthen the evaluation process. Our objective is not benchmark presentation alone; it is reliable real-world generalization. Our goal is to build a system that works reliably with real people.

Mathla follows a simple principle: an incorrect confident translation can be more harmful than silence. Where the system does not have enough evidence to produce a reliable output, uncertainty or silence should be preferred over fabricated translation. This is particularly important when the technology is eventually used in areas such as: • healthcare • education • government services • emergency communication • banking • employment

Sign-language data is sensitive because video may include a person's: • face • hands • body • identity • movement Mathla therefore separates consent choices and distinguishes between content that may be public and content that is intended only for training or research. The current platform already includes explicit consent, review controls, privacy policies, and separate treatment of training-only material.

Mathla is being built from the United Arab Emirates. Mathla's initial focus is Emirati and Arabic Sign Language, with the UAE serving as the launch environment for a platform designed to expand across the wider Arab world. The UAE provides a strong environment for: • accessibility innovation • AI research • government digitization • People of Determination initiatives • Arabic language development • education • community partnerships Our objective is to build something that can begin in the UAE and later serve Deaf communities throughout the Arabic-speaking world.

Mathla works with organizations across community development, accessibility, education, and research. Current relationships and engagements include: • Sawaed UAE Association for Culture and Community Empowerment — community and accessibility collaboration We are actively seeking additional partnerships with Deaf organizations, universities, government entities, researchers, technology companies, and accessibility specialists.

Mathla is an active development and research project. Today, the platform already supports: • community contribution • sign collection • human review • learning • dictionary development • campaigns • institutional participation At the same time, the AI translation system remains under active research and evaluation. We deliberately separate what works today from what we are still trying to solve. That distinction is important to us technically and ethically.

Our long-term goal is larger than a dictionary or isolated-sign recognizer. We want a Deaf person to be able to sign naturally to a phone, computer, smart display, or future wearable device and have that communication understood by someone who does not know sign language. And in the other direction, spoken or written language should eventually be converted into accessible signed communication. The result should feel less like using a translation tool and more like simply having a conversation.

Mathla is being built for: • Deaf sign-language users • families • teachers • schools • universities • healthcare providers • government organizations • accessibility teams • researchers • interpreters • community organizations • companies serving the public

Community before automation — Language belongs to people. Evidence before claims — Models must prove that they generalize. Safety before fluency — A system should not invent meaning. Privacy by design — Consent and control must be built into the platform. Arabic first — Arabic Sign Language is not an afterthought. Build for real life — The final measure is whether the technology improves communication outside the laboratory.

Platform: Live Web: Live iOS: Live on the App Store Android: Closed testing (coming soon) Community data collection: Active Arabic Sign Language dataset development: Active Continuous sign-language AI: Research and evaluation stage Institutional partnerships: Expanding

Mathla United Arab Emirates Website: mathla.org Email: [email protected] For partnerships, research collaboration, accessibility projects, data initiatives, or institutional pilots, please contact us.