Custom AI for Sign Language Recognition: MediaPipe, Transformer, CTC

Development of AI for Sign Language Recognition

Deaf employees in manufacturing often lack real-time information – alert systems are not adapted to sign language. Attempts to deploy off-the-shelf translators fail due to high gesture variability and lack of support for Russian Sign Language (RSL). We build custom AI solutions that solve this problem at the engineering pipeline level. Our company specializes in computer vision and NLP; we developed a pipeline using MediaPipe for keypoint extraction and a Transformer for classification, achieving accuracy up to 84% on isolated signs and WER as low as 17% on continuous signing. We have implemented over 10 sign language recognition projects, accumulating more than 5 years of experience.

Why is Continuous Sign Language Recognition Harder than Isolated Signs?

An isolated sign is a single gesture from a vocabulary (e.g., HELLO). Continuous Sign Language Recognition (CSLR) is a stream of signs with undefined boundaries, where adjacent signs influence each other. CSLR requires CTC decoding (Connectionist Temporal Classification), which predicts a sequence of characters without segmentation. An additional challenge is motion-based disambiguation: signs identical in shape differ only in movement trajectory. Humans handle this with context; neural networks use BiLSTM layers to analyze temporal dependencies.

How We Use MediaPipe and Transformer for Isolated Signs

Our pipeline extracts 258 features per frame: 21 keypoints per hand + 33 body landmarks (MediaPipe Holistic). Normalization relative to shoulder distance provides scale invariance – a gesture from 0.5 m is recognized the same as from 2 m. The Transformer processes a window of 30 frames (1 second) and outputs class probabilities. The code below shows architecture and inference.

import numpy as np
import cv2
import torch
import torch.nn as nn
import mediapipe as mp
from dataclasses import dataclass
from collections import deque
from typing import Optional
import json

@dataclass
class SignPrediction:
    sign_id: int
    gloss: str          # sign name (HELLO, WATER, THANK_YOU)
    confidence: float
    hand: str           # left / right / both
    frame_range: tuple  # (start_frame, end_frame)

class HandLandmarkExtractor:
    """
    Extracts 21 keypoints per hand + 33 body landmarks from MediaPipe.
    Normalization relative to shoulder distance for scale invariance.
    """
    def __init__(self):
        self.mp_holistic = mp.solutions.holistic
        self.holistic = self.mp_holistic.Holistic(
            static_image_mode=False,
            model_complexity=1,
            min_detection_confidence=0.5,
            min_tracking_confidence=0.5
        )

    def extract(self, frame: np.ndarray) -> Optional[np.ndarray]:
        """
        Returns feature vector of dimension 258:
        - 21 right hand points × 3 (x,y,z) = 63
        - 21 left hand points × 3 = 63
        - 33 body points × 4 (x,y,z,visibility) = 132
        """
        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = self.holistic.process(rgb)

        # Right hand
        rh = np.zeros(63, dtype=np.float32)
        if results.right_hand_landmarks:
            for i, lm in enumerate(results.right_hand_landmarks.landmark):
                rh[i*3:i*3+3] = [lm.x, lm.y, lm.z]

        # Left hand
        lh = np.zeros(63, dtype=np.float32)
        if results.left_hand_landmarks:
            for i, lm in enumerate(results.left_hand_landmarks.landmark):
                lh[i*3:i*3+3] = [lm.x, lm.y, lm.z]

        # Body pose
        pose = np.zeros(132, dtype=np.float32)
        if results.pose_landmarks:
            for i, lm in enumerate(results.pose_landmarks.landmark):
                pose[i*4:i*4+4] = [lm.x, lm.y, lm.z, lm.visibility]

        features = np.concatenate([rh, lh, pose])

        # Normalization: shoulder distance as scale
        # Points 11 (left shoulder) and 12 (right shoulder) in pose
        left_shoulder = pose[11*4:11*4+2]
        right_shoulder = pose[12*4:12*4+2]
        shoulder_dist = np.linalg.norm(left_shoulder - right_shoulder)
        if shoulder_dist > 0.01:
            features[:126] /= shoulder_dist  # normalize only hands

        return features if (results.right_hand_landmarks or
                            results.left_hand_landmarks) else None


class SignLanguageTransformer(nn.Module):
    """
    Transformer for keypoint sequences.
    Input: (batch, seq_len, 258) – window of 30–60 frames.
    Trained on WLASL (2000 ASL signs) or PHOENIX-2014T (German).
    """
    def __init__(self, input_dim: int = 258,
                 d_model: int = 256,
                 nhead: int = 8,
                 num_layers: int = 4,
                 num_classes: int = 2000,
                 dropout: float = 0.1):
        super().__init__()
        self.input_proj = nn.Linear(input_dim, d_model)
        self.pos_emb = nn.Embedding(300, d_model)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout, batch_first=True
        )
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.classifier = nn.Sequential(
            nn.LayerNorm(d_model),
            nn.Linear(d_model, d_model // 2),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(d_model // 2, num_classes)
        )

    def forward(self, x: torch.Tensor,
                 src_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        """x: (B, T, 258)"""
        B, T, _ = x.shape
        positions = torch.arange(T, device=x.device).unsqueeze(0).expand(B, -1)
        x = self.input_proj(x) + self.pos_emb(positions)
        x = self.encoder(x, src_key_padding_mask=src_key_padding_mask)
        x = x.mean(dim=1)
        return self.classifier(x)


class SignLanguageRecognizer:
    """
    Real-time isolated sign recognition.
    Sliding window buffer + threshold-based sign start/end detection.
    """
    WINDOW_SIZE = 30   # 30 frames @ 30 fps = 1 second
    MIN_SIGN_FRAMES = 10

    def __init__(self, model_path: str,
                  vocabulary_path: str,
                  confidence_threshold: float = 0.7,
                  device: str = 'cuda'):
        self.device = device
        self.extractor = HandLandmarkExtractor()

        with open(vocabulary_path) as f:
            self.vocabulary = json.load(f)  # {id: gloss}

        self.model = SignLanguageTransformer(
            num_classes=len(self.vocabulary)
        ).to(device)
        checkpoint = torch.load(model_path, map_location=device)
        self.model.load_state_dict(checkpoint['model_state_dict'])
        self.model.eval()

        self.threshold = confidence_threshold
        self.frame_buffer: deque = deque(maxlen=self.WINDOW_SIZE)
        self.sign_active = False
        self.sign_start_frame = 0
        self.frame_count = 0

    def process_frame(self, frame: np.ndarray) -> Optional[SignPrediction]:
        self.frame_count += 1
        features = self.extractor.extract(frame)

        if features is None:
            if self.sign_active and len(self.frame_buffer) >= self.MIN_SIGN_FRAMES:
                return self._classify_buffer()
            self.frame_buffer.clear()
            self.sign_active = False
            return None

        self.frame_buffer.append(features)
        self.sign_active = True

        if len(self.frame_buffer) < self.WINDOW_SIZE:
            return None

        return self._classify_buffer()

    @torch.no_grad()
    def _classify_buffer(self) -> Optional[SignPrediction]:
        seq = np.stack(list(self.frame_buffer))  # (T, 258)
        tensor = torch.from_numpy(seq).unsqueeze(0).float().to(self.device)

        logits = self.model(tensor)
        probs = torch.softmax(logits, dim=-1).squeeze()
        conf, pred_id = probs.max(dim=0)
        conf = float(conf.item())
        pred_id = int(pred_id.item())

        if conf < self.threshold:
            return None

        gloss = self.vocabulary.get(str(pred_id), f'SIGN_{pred_id}')
        return SignPrediction(
            sign_id=pred_id,
            gloss=gloss,
            confidence=round(conf, 3),
            hand='both',
            frame_range=(self.frame_count - len(self.frame_buffer),
                         self.frame_count)
        )

How Our CSLR Pipeline Works

For continuous signing we use CNN + BiLSTM + CTC. Input – windows of 90 frames (3 seconds). CTC decoder outputs a sequence of glosses, removing repeats and blank symbols. Our experience shows that the key difficulty is fingerspelling (manual alphabet). Letters are shown sequentially, and standard CSLR often confuses them. We solve this with a separate lightweight classifier based on MediaPipe, which processes each frame and corrects the main decoder's output. This reduces WER by 5–7% in tests on PHOENIX-2014T.

Our Transformer approach is 1.5–2× faster than I3D with comparable accuracy. For CSLR we use an ensemble of BiLSTM with attention, yielding WER 3–5% lower than standard non-segmented CTC. According to WLASL-2000, Transformer achieves 84% on isolated signs.

What the Work Includes

• Requirements analysis: vocabulary definition (number of signs, specifics – RSL, ASL, BSL), data collection and annotation.
• Model training: architecture selection (Transformer / BiLSTM + CTC), augmentation, fine-tuning on the target dataset.
• Inference optimization: quantization (INT8), export to ONNX, p99 latency reduction to 30–50 ms.
• Integration: REST API, RTSP stream, desktop application.
• Documentation and training: handover of model card, instructions for adapting to new signs.

Project Workflow

1. Data audit – assess quality and sufficiency of video material (1–2 weeks).
2. Prototyping – baseline model on 50–100 signs (2–4 weeks).
3. Deployment – CI/CD, MLflow tracking, drift monitoring (2–3 weeks).
4. Iteration – fine-tuning on real customer data (4–8 weeks).

Indicative Timelines

Cost is calculated individually – depends on data volume, number of classes, and required accuracy. To evaluate your project, contact us – we will provide a preliminary plan and timeline within 2–3 days. We have been working with sign languages for over 5 years; our engineers are certified in MediaPipe and PyTorch. Get a consultation to discuss the details.