Extracting Named Entities (NER) in a Mobile Application
Our NER mobile app solutions integrate named entity recognition for entity extraction on iOS and Android. We utilize spaCy NER for lightweight tasks and Hugging Face NER for high accuracy, with custom NER model training for domain-specific entities. The hybrid NER regex approach combines TFLite NER on-device with server-side APIs for smart form autofill and NER API integration across platforms. This enables efficient text processing in apps and seamless NER model training.
In a delivery app, a user types "deliver on Thursday at 14:00 to Lenina 5 apt 12" into a single field. Without NER, you'd need dozens of regular expressions — and every non-standard format breaks the logic. We solve this with a custom NER model: it transforms free-form text into structured fields {date, time, street, building, apartment}. Input time savings — up to 40%, and error rate drops by 60%. Basic integration costs $2,000–$5,000, and full integration with custom models is $10,000–$20,000. Basic integration takes 3 to 5 days, full deployment with smart forms — 2 to 3 weeks. Cost is calculated individually for your case.
How NER Converts Text to Structured Data
NER (Named Entity Recognition) is an NLP task that extracts entities from text: names, locations, dates, organizations. In mobile apps, NER is used for:
- Smart forms and autofill. The user writes a message to the courier — the app parses the address and delivery time without separate form fields.
- Search with filters. "iPhone 15 Pro 256GB black" -> {brand: Apple, model: iPhone 15 Pro, storage: 256GB, color: black}. A structured query is more accurate than full-text search.
- Chatbots and voice assistants. Extraction of parameters from free speech or text to fill dialog slots.
- Receipt and document processing. OCR text from a receipt -> {store, amount, date, items}.
Why Choose a Hybrid Approach?
Pure transformer-based NER provides high accuracy but is slow and requires a server. Pure regex is fast but breaks on free text. On-device NER is up to 10x faster than server API, with zero latency and no network costs. A hybrid regex+NER approach combines the speed of rule-based extraction for standard entities (phones, emails, SKUs) with ML flexibility for complex contexts. In most projects, this is the optimal balance of performance and quality.
Technical Approaches for Mobile Applications
| Approach | Speed | Domain Accuracy | Offline | Recommended Scenario |
|---|---|---|---|---|
| spaCy + custom model | 5–20 ms | Medium (fine-tuned high) | Yes (model up to 50 MB) | Real-time autofill, simple entities |
| Transformer (Hugging Face) | 50–200 ms | High | No (usually server) | Complex contexts, legal/medical texts |
| Regex + NER hybrid | 1–5 ms | High for formats, low for free text | Yes | Phones, emails, SKUs + NER for the rest |
Comparison of on-device vs server-side NER:
| Criterion | On-device (TFLite/CoreML) | Server API (transformer) |
|---|---|---|
| Latency | 1–20 ms | 50–200 ms + network |
| Offline | Yes | No |
| Model size | Up to 20 MB | 200+ MB |
| Accuracy on complex domains | Medium | High |
| Model update | Through App Store | Without app re-release |
spaCy + Custom NER Model
spaCy is a standard for production NER. The base Russian model ru_core_news_lg recognizes persons, organizations, locations, dates. For domain-specific entities (clothing sizes, SKUs, medical terms), fine-tuning is required. We use BIO tagging scheme and conditional random fields (CRF) for sequence labeling.
Example of training a spaCy model
import spacy from spacy.training import Example # Load base Russian model nlp = spacy.load("ru_core_news_lg") # Add custom entity types ner = nlp.get_pipe("ner") ner.add_label("PRODUCT_SIZE") ner.add_label("PRODUCT_COLOR") ner.add_label("ARTICLE") # Training example TRAIN_DATA = [ ("I want to find sneakers size 42 in blue color SKU 98765", {"entities": [(33, 35, "PRODUCT_SIZE"), (43, 49, "PRODUCT_COLOR"), (59, 64, "ARTICLE")]}), ] optimizer = nlp.resume_training() for text, annotations in TRAIN_DATA: doc = nlp.make_doc(text) example = Example.from_dict(doc, annotations) nlp.update([example], sgd=optimizer) Official spaCy NER training documentation: https://spacy.io/usage/training#ner
Transformer-based NER via Hugging Face
For high accuracy on complex domains, we use a fine-tuned DeepPavlov/rubert-base-cased with a token classification head and attention mechanisms. This approach is slower than spaCy but handles contextual dependencies significantly better.
from transformers import pipeline ner_pipeline = pipeline( "token-classification", model="DeepPavlov/rubert-base-cased-ner", aggregation_strategy="simple" ) def extract_entities(text: str) -> list[Entity]: raw_entities = ner_pipeline(text) return [ Entity( text=e["word"], label=e["entity_group"], confidence=e["score"], start=e["start"], end=e["end"] ) for e in raw_entities if e["score"] > 0.7 ] How to Implement a Hybrid Extractor?
import re from typing import NamedTuple class EntityExtractor: PHONE_PATTERN = re.compile(r'(?:\+7|8)[\s\-]?\(?\d{3}\)?[\s\-]?\d{3}[\s\-]?\d{2}[\s\-]?\d{2}') EMAIL_PATTERN = re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b') DATE_PATTERN = re.compile(r'\b(\d{1,2})[./](\d{1,2})(?:[./](\d{2,4}))?\b') def extract_all(self, text: str) -> dict: phones = self.PHONE_PATTERN.findall(text) emails = self.EMAIL_PATTERN.findall(text) ner_entities = extract_entities(text) locations = [e.text for e in ner_entities if e.label in ("LOC", "GPE")] persons = [e.text for e in ner_entities if e.label == "PER"] return { "phones": phones, "emails": emails, "locations": locations, "persons": persons } Mobile Integration
iOS: NER for Smart Form Filling
// iOS: NER via server API with form autofill class AddressFormViewModel: ObservableObject { @Published var street = "" @Published var building = "" @Published var apartment = "" @Published var deliveryTime = "" func parseFromText(_ userText: String) { Task { let entities = try await nerApi.extract(text: userText) await MainActor.run { if let address = entities.first(where: { $0.label == "ADDRESS" }) { parseAddressComponents(address.text) } if let time = entities.first(where: { $0.label == "TIME" }) { deliveryTime = time.text } } } } } On-device NER via CoreNLP or TFLite
For simple domain entities (SKUs, sizes, colors), you can deploy a compact TFLite NER model (< 20 MB) directly on the device. This removes latency and works offline.
Apple's NaturalLanguage.framework with NLTagger supports basic entity types out of the box for Latin texts:
let tagger = NLTagger(tagSchemes: [.nameType]) tagger.string = userInput tagger.enumerateTags(in: userInput.startIndex..<userInput.endIndex, unit: .word, scheme: .nameType, options: [.omitWhitespace]) { tag, range in if let tag = tag { print("Entity: \(userInput[range]), type: \(tag.rawValue)") } return true } For Russian, NLTagger performs noticeably worse — we use it only as a prefilter or for apps with Latin text.
What's Included in the Work
We offer a full-cycle NER integration into your mobile app:
- Analysis of the subject area and definition of target entities.
- Collection and annotation of training data (from 500 examples per entity).
- Architecture selection: spaCy, transformer, or hybrid.
- Model development and training.
- Integration into the mobile client (iOS/Android) via API or on-device.
- Creation of smart UI components: form autofill, entity highlighting, search filters.
- Testing on real data and latency optimization.
- Deliverables: documentation, model artifacts (spaCy pipelines or TFLite files), API endpoints for NER, mobile SDK (Swift/Kotlin wrappers), training sessions for your team, and 3 months of support.
Timeframe Estimates
We follow a clear integration process:
- Analysis of subject area and entity definition — 1 day.
- Data collection and annotation — 1 week.
- Architecture selection — 1 day.
- Model development and training — 1 week.
- Mobile integration — 1 week.
- UI components creation — 3 days.
- Testing and optimization — 2 days.
- Documentation and training — 2 days.
- Basic NER with a ready Russian model + API — 3–5 days.
- Fine-tuning on custom entities — 1–2 weeks.
- Full integration into mobile UI (smart forms, search, chatbot) — 2–3 weeks.
- On-device model (TFLite/CoreML) — additional 1–2 weeks.
We have over 6 years of experience in mobile development and have completed more than 30 NLP projects. We guarantee high accuracy with certified models and provide a warranty on all deliverables. If you need NER integration into your mobile app — contact us for a project assessment. We will calculate the cost and timeline for your case and suggest the optimal solution. Get a free consultation.