Tesseract

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

Prescription parsing typically follows a two-stage process: an OCR engine converts the prescription image into raw text, then a large language model performs semantic extraction

基于VLM的OCR相比Tesseract等传统OCR引擎，在上下文理解、复杂版面处理和多语言支持方面有明显优势

Tesseract is an open-source OCR engine originally developed by HP Labs and later maintained by Google, with a history of over 30 years.

For mixed-language documents, it is usually necessary to run different language models separately and compare the results, taking the result with higher confidence.

Tesseract is an open-source OCR engine that supports more than 100 languages, including Hindi (language code hin) and English (eng).

Each word output by Tesseract comes with a confidence score, which can be used to flag low-confidence words for human review.

Tesseract has limited table recognition capability and often parses table structures into linear text, so additional table-line detection and cell reconstruction steps are needed.

Spartan's OCR (Tesseract or a plug-in engine) is used only for scanned pages.

The core output of OCR is a stream of characters; it does not understand the business meaning of the text, and typical tools include Tesseract, ABBYY FineReader, and the OCR services of major cloud providers.

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.

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

Mainstream OCR engines (such as Tesseract, Google Vision, AWS Textract) return a confidence value between 0 and 1 for each recognized word or character.

Mainstream tools in the traditional OCR space include Tesseract (open source), ABBYY FineReader, and OCR APIs provided by various cloud vendors.

Modern OCR systems such as Tesseract, Google Document AI, and AWS Textract provide spatial coordinates and confidence scores at the granularity of lines, words, and even characters.

Traditional document processing pipelines are composed of multiple independent components connected in series—such as OCR engines, rule engines or regex-based extraction, and data validation layers—each with its own independent failure points.

Modern on-device AI models can combine OCR and semantic understanding into a single inference pass, reducing latency compared to traditional OCR tools such as Tesseract, which require a separate run followed by a call to a translation interface.