trocr-large-printed
by microsoft
Transformer-based OCR for printed text recognition
microsoft/trocr-large-printedmixpeek://image_extractor@v1/microsoft_trocr_large_v1Overview
TrOCR is an end-to-end text recognition model that uses a pre-trained image Transformer (DeiT) as the encoder and a pre-trained language model (RoBERTa) as the decoder. The large variant achieves state-of-the-art on printed text benchmarks.
On Mixpeek, TrOCR extracts readable text from images and video frames, making text-in-image content searchable through natural language queries.
Architecture
Encoder-decoder transformer: DeiT-Large (24 layers) as image encoder, RoBERTa-Large (24 layers) as text decoder. Pre-trained on large-scale synthetic printed text data, fine-tuned on SROIE and IAM datasets.
Mixpeek SDK Integration
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
namespace_id: "my-namespace",
collection_name: "my-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "ocr",
version: "v1",
parameters: { model_id: "microsoft/trocr-large-printed" },
},
});Capabilities
- High-accuracy printed text recognition
- End-to-end pipeline (no separate detection step)
- Multi-line text extraction
- Robust to noise, blur, and varying fonts
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| SROIE (text recognition) | Word Accuracy | 96.1% | Li et al., 2023 — Table 3 |
| IAM Handwritten | CER | 3.4% | Li et al., 2023 — Table 2 |
Performance
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Specification
Research Paper
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
arxiv.orgBuild a pipeline with trocr-large-printed
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