I Spent May Evaluating Different Engines for OCR
The article discusses an evaluation of various OCR engines conducted by the author, testing fourteen different engines on ninety-three documents. The findings indicate that there is no single best OCR engine, with different models excelling in different areas. The author emphasizes the importance of selecting the right OCR solution based on document type and cost-effectiveness.
- ▪The author tested fourteen OCR engines on ninety-three documents of varying difficulty.
- ▪Tesseract is noted for its speed and cost-effectiveness for clean high-volume documents.
- ▪For mixed production documents, Gemini Flash was identified as the best all-rounder in the tests.
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/i-spent-may-evaluating-different-engines-for-ocr/ |
| Publication time | Wed, 03 Jun 2026 16:30:00 +0000 |
| Retrieval time | 2026-06-03T16:37:50.590Z |
| Last seen | 2026-06-03T16:37:50.590Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | zn2K_VS2Rhsx |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Machine Learning I Spent May Evaluating Different Engines for OCR Testing fourteen engines on ninety-three human documents Ida Silfverskiöld Jun 3, 2026 17 min read Share Just for fun - there is an actual scatter chart further down | Images by author Not all documents were supposed to be read by a machine. Old hotel invoices, bank statements, payslips, loan applications, medical bills, customs forms, court filings, work orders. Most companies use free tools alongside paid APIs to try to convert these documents, and if you want structured output, APIs like Textract Structured run you up to around $65 per 1k pages.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.