PaddleOCR

Modern OCR engines such as Tesseract and ABBYY FineReader have already achieved accuracy rates above 95% for plain text recognition.

归一化处理中PDF解析可使用PyMuPDF或pdfplumber，Word文档处理常用python-docx，图片OCR可用Tesseract或PaddleOCR

PaddleOCR is an open-source optical character recognition tool library in Baidu's PaddlePaddle ecosystem that supports multiple languages, tables, and layout analysis, and can be deployed locally and run offline.

Invoice AI uses PaddleOCR to extract text from invoice images and identify key fields such as vendor information, invoice number, date, subtotal, tax amount, and total.

In the OCR baseline's approved invoices, 32% of line-item descriptions contain typos (such as "費" being read as "费"), which arithmetic validation cannot detect.

Zero-shot Qwen2.5-VL-3B-Instruct achieved a full-invoice exact rate of 5.3% and an auto-approval rate of 8.1% on the test set; the OCR + rules baseline achieved a full-invoice exact rate of 37.8% and an auto-approval rate of 55.0%.

The document processing layer uses RapidOCR (based on converted PaddleOCR models, inference via ONNX Runtime) together with PyMuPDF, reading the embedded text layer directly for vector PDFs, falling back to the OCR pipeline for scanned documents, and reconstructing layout structure through spatial bounding box clustering.

Realist uses PaddleOCR as an optional OCR layer to extract text such as model numbers and labels from photos and compare them against the listing; both the model and PaddleOCR use the Apache-2.0 license.

The OCR route commonly uses tools such as Tesseract and PaddleOCR to first obtain plain text and then hand it to a language model to determine the fields; end-to-end vision models (such as Donut and Qwen-VL) skip explicit OCR and predict structured output directly from the image.

A pragmatic minimum viable solution includes: periodically capturing the chat area via OCR to recognize text, maintaining a FAQ keyword mapping table, judging with string matching or lightweight intent classification, simulating keyboard input to send preset answers, and setting a frequency cap.

Traditional engines such as Tesseract suit rule-based text, open-source models such as PaddleOCR and EasyOCR suit Chinese and general scenarios, and cloud APIs and vision large models suit complex layouts and unstructured content.

InvoicePilot AI's planned technology stack includes Ollama, Qwen2.5 or other compatible open-source models, PaddleOCR, Python + FastAPI, SQLite, and React.

PaddleOCR is an OCR tool library open-sourced by Baidu's PaddlePaddle team, supporting text detection and recognition in multiple languages including Chinese and English, with specialized optimization for scenarios such as scanned documents, tilted photos, and low-resolution receipts.

Compared with traditional solutions such as Tesseract, PaddleOCR has higher accuracy on Chinese and complex layouts, and provides lightweight models for CPU inference.

The study incorporates actual outputs from three OCR engines: PaddleOCR, EasyOCR, and Tesseract.

Modern OCR engines typically combine convolutional neural networks (CNNs) for character feature extraction with recurrent neural networks (RNNs) or Transformers to model the contextual relationships of character sequences

PaddleOCR is an open-source OCR toolkit from Baidu that excels at Chinese text recognition and supports batch processing.

Engineering drawing OCR typically requires domain-adaptive fine-tuning on top of general-purpose engines such as Tesseract and PaddleOCR.

The history of OCR technology can be traced back to the 1970s; after the 2010s, deep learning-based OCR engines (such as Tesseract 4.0 and PaddleOCR) significantly improved recognition accuracy.

In resume parsing, image-based PDFs (scanned documents) require the introduction of OCR technology such as Tesseract or Baidu PaddleOCR, while Word documents need to be parsed via Apache POI in OOXML format.