JPEG Text Transcription & Summarization

I have a set of JPEG files that must be converted into clean, error-free digital text so the content can feed directly into a Natural Language Processing pipeline focused on text summarization. Accuracy is critical; even minor mistakes will ripple through the NLP stage and distort the summaries I plan to generate.

Here is how the work should unfold:
• Extract every word from each image, preserving paragraph breaks and any obvious headings.
• Deliver the transcribed text in UTF-8 plain-text or editable Word format—whichever you prefer—ready for immediate ingestion by my summarization scripts.
• Flag unreadable portions with clear markers so I can cross-check the source images.

Acceptance criteria
• 99%+ character-level accuracy when compared to the original JPEGs.
• Consistent formatting (no stray line breaks, no mixed encodings).
• Final files pass a quick diff check against a secondary OCR run I’ll perform for verification.

If you routinely work with OCR tools such as Tesseract, ABBYY FineReader, or Google Vision and can combine that with a meticulous manual proofread, this will be straightforward. Turnaround and previous samples of similar high-accuracy transcription work will help me decide quickly.