Systematic, auditable coding of signs and multimodal data. Collect in the field by phone or import an existing corpus at your desk — you set the framework, review every result, and export a full audit trail.
Upload any number of images and DeCiphr does the groundwork: every item is assigned a unique admin ID, sorted into a clear order, and any image containing multiple distinct visual items is split so each is independently numbered and catalogued. Human faces are detected and blurred on your device by default before any image is transmitted or stored — an ethical safeguard for research in public space, which you can turn off when appropriate (for example, public figures on public signage). Your corpus is organized and ready before any analysis begins.
Define your own coding scheme. Works with any established or original framework. Share as a portable .sigframe file with collaborators.
Analysis runs on Anthropic's Claude vision API: the model reads each image and proposes codes from the framework you defined. You review and edit every output, and the exact model, prompt, and raw response are logged for each sign. It runs in strict coding mode, so the AI applies only the codes you defined.
Capture public signs in the field with automatic GPS tagging. View all your signs plotted on an interactive map, organized by project.
Write interpretive field notes directly on each sign. Every AI prompt and raw response is logged alongside your edits — giving you a complete, exportable record of every analytical decision for full methodological transparency.
Export structured CSV files that import into NVivo, Atlas.ti, and MAXQDA, ready for your existing research workflow.
Every analysis leaves a complete paper trail: the exact prompt sent to the AI, the full raw response, and a timestamped record of every edit you made to the output. All of it is exportable as part of your dataset.
Build your coding scheme from scratch, or load an established framework shared as a .sigframe file. Define codes, values, and a master prompt for the AI.
Capture signs in the field with GPS tagging, or import screenshots and digital images. DeCiphr automatically assigns each item a unique admin ID, sorts your corpus into order, and separates any image containing multiple visual items into independently numbered entries. Any human faces are detected and blurred on-device by default before storage or transmission.
DeCiphr sends each image to Claude Vision and transcribes and translates language visible on the image. Then it proposes codes from your framework. You then review, edit, and annotate every result — and the prompt and raw response are saved to the audit trail.
Export a complete CSV with all coded values, languages detected, GPS coordinates, analytic memos, and metadata.
DeCiphr is built by an active field researcher, and its design is grounded in the methodological demands of linguistic landscape research — from systematic data collection in public spaces to structured, framework-driven analysis. Whether working in the field with a phone or processing a corpus at a desk, the workflow is the same.
Understand exactly what the AI is doing — and what it isn't. Here are the answers to some of the questions that matter methodologically.
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Linguistic landscape (LL) research has always been shaped by the tools available for capturing, organizing, and interpreting public signage, and each technological advance has widened what researchers are able to document and ask. This is evident in highly multilingual settings, where the field researcher may not be competent in all the languages available in the public space, leading to either hiring translators or adopting digital tools able to recognize unfamiliar scripts.
One of the first technological contributions addressed location and collection. Barni and Bagna’s (2009) MapGeoLing used smartphone GPS to locate signs on a map, enabling quantitative analysis of linguistic distribution across space. Mobile applications then standardized the photographic capture and geotagging of signs: LinguaSnapp (Gaiser & Matras, 2021) built a structured LL archive, while Lingscape (Purschke, 2017) adopted a citizen-science model in which members of the public crowdsource sign data.
Another contribution concerns analysis. Mignone (2025) used the qualitative data analysis package ATLAS.ti to build a coding system grounded in geosemiotics, uploading photographs and assigning codes manually alongside interview transcripts and fieldnotes within a single project. This integration supports queries and co-occurrence analysis across a dataset, but the coding itself remains a manual, researcher-driven act. More automated approaches have come from geography and urban studies: Hong (2020) applied computer vision and machine learning to extract and code text from online street-level imagery in order to demonstrate linguistic diversity across urban space.
Most recently, attention has turned to artificial intelligence. Voss (2024) proposes generative AI for indexing, hypothesis generation, and interpretive support, arguing that it should function as a research assistant in dialogue with the researcher rather than as a replacement for analytical judgment: “Researchers must use AI technologies responsibly and consider them as tools to augment human expertise rather than replace it” (p. 419). This framing sharpens the central methodological question for the field: what can AI do with LL data? At a minimum, it can identify signs, transcribe, and translate text, and it can process data according to researcher-defined coding—provided its interpretation is constrained to predetermined codes rather than left open.
DeCiphr is the tool I have developed in response to this question. It is a smartphone and web application for coding LL data in which the researcher creates or imports a coding framework and the AI codes within it. As a mobile application, it is designed as a fieldwork companion in multilingual contexts, processing signs as they are collected—translating and coding on site, in precisely the conditions my own project demands. Each framework defines its codes, definitions, and permissible values, and is organized into two layers.
Taking geosemiotics (Scollon & Wong Scollon, 2003) as an example, Layer 1 holds the deductive codes the AI assigns: seven geosemiotic codes including code preference, inscription, emplacement, represented participants, modality, composition, and interactive participants. Here the AI records only what is visually present—languages, layout, font, and composition—and returns “Uncodable” wherever the visual evidence does not warrant a confident assignment. Layer 2 holds interpretive questions that the researcher alone answers, drawing on fieldwork presence, community knowledge, and ethnographic judgment. The AI cannot answer these by design, and the researcher retains authority throughout, able to edit both the processing and the Layer 1 output.
This division clarifies both the potential and the limitations of AI for qualitative LL research. Its potential is considerable: it can manage large data, support translation across unfamiliar languages, and give the researcher more time to concentrate on interpretation, while its guiding questions can scaffold students and less experienced researchers. However, it can be argued that the same guiding questions that scaffold early researchers may also produce shallow or restrictive responses. Another issue to consider is agency: allowing the researcher to edit AI output distributes agency and accountability across human and machine in ways that are difficult to disentangle. It is also important to remember that large language models are not trained evenly across cultures, and this unevenness affects coding, transcription, and translation alike.
Taken together, these tools trace a clear trajectory from locating and collecting signs, to organizing and querying them, to coding them. DeCiphr extends that trajectory into AI-assisted coding while deliberately preserving the researcher’s interpretive authority. Treated as an enhancement to the researcher’s experience, AI can strengthen the reflexive, qualitative work at the heart of LL research without displacing the researcher’s role.
Barni, M., & Bagna, C. (2009). A mapping technique and the linguistic landscape. In E. Shohamy & D. Gorter (Eds.), Linguistic landscape: Expanding the scenery (pp. 126–140). Routledge.
Gaiser, L., & Matras, Y. (2021). LinguaSnapp Manchester: Multilingualism revealed. University of Manchester.
Hong, S.-Y. (2020). Linguistic landscapes on street-level images. ISPRS International Journal of Geo-Information.
Mignone, O. (2025). Vital signs: Tibetan in the linguistic landscape of Jackson Heights [Doctoral dissertation, City University of New York].
Purschke, C. (2017). Crowdsourcing the linguistic landscape: Introducing Lingscape. Linguistik Online, 85(6).
Scollon, R., & Wong Scollon, S. (2003). Discourses in place: Language in the material world. Routledge.
Voss, E. (2024). Artificial intelligence and linguistic landscape research: Affordances, challenges & considerations. Linguistic Landscape, 10(4), 400–424.
How to cite: Al-Ajmi, S. M. (2026). DeCiphr: An AI Research Assistant [Computer software]. https://deciphr.tech/
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