---
title: 'PastReader: Historical Spanish OCR Task'
url: https://www.emergentmind.com/topics/pastreader
type: topic
---

# PastReader: Historical Spanish OCR Task

Searching arXiv for papers directly about “PastReader” to ground the article in the relevant literature.
PastReader is an IberLEF 2025 shared task on **transcribing texts from the past**, centered on OCR for **historical Spanish newspaper and periodical pages** whose visual and documentary properties make them difficult to process automatically. In the reported participation study by the GRESEL team, the task is framed as a realistic historical-document transcription problem involving degraded scans, mixed layouts, illustrations, stains, low contrast, and decorative typography, and is approached through a comparative evaluation of three distinct paradigms: a historical OCR platform, a conventional OCR engine, and a compact multimodal model [2507.04878].

## 1. Task definition and operational scope

PastReader is organized around **two subtasks**. **Task 1** is **clean OCR post-correction**, in which participants are given intentionally noisy OCR outputs in plain text and are asked to clean them so that they better match the ground truth. **Task 2** is **end-to-end OCR from PDF files**, in which participants ignore the provided OCR text and instead produce transcription directly from the **PDF page images**. The GRESEL study participated only in **Task 2**, primarily because of time constraints and because the comparison of complete OCR pipelines was a central objective [2507.04878].

The task is defined by the difficulty of historical Spanish print culture rather than by a single document genre or a narrowly controlled image condition. The source material comprises **historical Spanish newspaper and periodical pages** whose visual state and editorial structure introduce OCR failure modes that are not incidental but constitutive of the benchmark. The paper emphasizes degraded scans, mixed layouts, illustrations, stains, low contrast, decorative typography, handwritten marginalia, stamps, and non-linear reading order. A plausible implication is that PastReader functions not merely as an OCR benchmark, but as a stress test for the interaction among document layout analysis, transcription fidelity, and historically variable orthography.

A recurrent misconception would be to treat PastReader as a standard OCR task on noisy but otherwise regular pages. The reported evidence argues against that simplification: pages may contain **little or no text**, **images interrupting text flow**, **decorative elements**, or complex layout structures that disrupt conventional reading order. This makes the task materially different from OCR on modern, clean text.

## 2. Documentary basis and corpus heterogeneity

The corpus used in the GRESEL report consists of **isolated pages from 8 periodical publications from the 19th and 20th centuries**. The publications span a wide range of genres, including **general news and cultural magazines**, **satirical/humorous magazines**, **spiritualist chronicles**, **serial fiction and poetry**, **fashion/feminine customs**, and **scientific/medical publications**. The resulting variation is multidimensional: the pages differ in **font styles**, **layout complexity**, **paper color/background tone**, **print quality**, **stains and degradation**, **handwritten marginalia**, **stamps and illustrations**, and **decorative initials and small caps** [2507.04878].

This heterogeneity is methodologically important because it constrains what can be inferred from aggregate OCR scores. A system may perform adequately on ordinary running text while failing on ornate pages, columnar layouts, or documents with sparse textual content. The paper explicitly argues that robust performance would likely require a carefully curated historical Spanish training set with **philological and paleographic transcription guidelines**, which had not yet been created for the project. This suggests that PastReader is as much a corpus-design and editorial-normalization problem as it is a model-selection problem.

The shared task therefore occupies a space between document analysis and historical text scholarship. Its practical stakes include not only recognition accuracy, but also preservation of original lineation, orthographic form, and layout-sensitive reading order.

## 3. Evaluation protocol and metric structure

The shared-task evaluation used **eight metrics**, although the organizers had initially planned six. The final metric set comprised **Levenshtein distance**, **WER**, **NED**, **BLEU**, **ROUGE-1**, **ROUGE-2**, **ROUGE-L**, and **ROUGE-LSum** [2507.04878].

| Metric family | Metrics | Interpretation in the report |
|---|---|---|
| Literal similarity | Levenshtein distance, WER, NED | Lower is better; rewards exact character/word reproduction |
| Overlap / semantic preservation | BLEU, ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-LSum | Higher is better; measures broader textual or semantic overlap |

The paper characterizes **Levenshtein distance**, **WER**, and **NED** as measures of literal similarity, and **BLEU** and **ROUGE** as measures of overlap and broader textual or semantic preservation. No explicit formulas are printed in the report, but the interpretations are standard: **WER** reflects substitution, insertion, and deletion at the word level; **Levenshtein** counts edit operations; **NED** normalizes edit distance by length; and **BLEU/ROUGE** assess n-gram overlap and recall-oriented similarity.

One of the clearest methodological findings is that evaluation is highly sensitive to formatting decisions. In the Transkribus runs, preserving line breaks improved **literal/precision-oriented metrics**, whereas joining lines improved **semantic metrics**. This demonstrates that OCR quality in PastReader cannot be interpreted independently of output normalization and segmentation policy.

## 4. Compared system paradigms

The GRESEL report evaluates three OCR paradigms under **consumer-grade hardware** constraints: **Transkribus**, **Tesseract**, and **Granite3.2-vision:2b**. The study is explicitly presented as a comparative exploration rather than as the introduction of a single new system [2507.04878].

| Submitted run | Method | Configuration |
|---|---|---|
| GRESEL1_run1 / GRESEL2_run2 | Transkribus | Historical Spanish model **Coloso Español**; line breaks preserved vs. joined |
| GRESEL1_run2 / GRESEL1_run3 | Tesseract 5.5.0 | Baseline vs. fine-tuned on task data |
| GRESEL2_run1 | Granite3.2-vision:2b | Fine-tuned with **QLoRA** |

### Transkribus

The Transkribus experiment used the historical Spanish model **Coloso Español** in an inference-only setting. The workflow was to submit PDF pages, obtain OCR output, export two variants—one preserving **line breaks** and one **joining lines**—and convert them to the required TXT format. The paper notes that Transkribus is attractive for historical documents because it supports layout annotation, collaborative transcription, and model training.

### Tesseract

The Tesseract experiment used **Tesseract 5.5.0** on Windows, with one off-the-shelf baseline and one fine-tuned variant. The training workflow included renaming transcripts to the `.gt.txt` convention, converting PDFs to **TIFF** at roughly **300 DPI**, generating `.box` files, creating `.lstmf` files with `--psm 6` and `--oem 1`, building `list.txt`, training from `spa.traineddata`, and packaging with `combine_tessdata`. Development experiments used subsets of **100**, **1,000**, **2,000**, and the full dataset. Despite this, the non-fine-tuned baseline consistently performed best during development.

### Granite3.2-vision:2b

The multimodal system used **Granite3.2-vision:2b**, fine-tuned with **QLoRA**, **4-bit NF4 quantization**, double quantization, **bfloat16** computation, LoRA adapters on the projection layers, gradient checkpointing, batch size **1**, and gradient accumulation **8**. The reported hardware was an **Nvidia RTX 5080**, **16 GB VRAM**, and **32 GB RAM**. Because full-resolution images exhausted memory, pages were resized to approximately **414 × 585 pixels**, preserving aspect ratio and padding to a fixed size. The model was prompted in a chat-style format with a system prompt instructing exact extraction, no correction or interpretation, no invented content, and only raw text output.

## 5. Reported results and observed failure modes

The Transkribus runs provide the sharpest evidence for metric-format interaction. **GRESEL1_run1**, which preserved line breaks, performed better on **Levenshtein: 105.1823**, **WER: 0.2933**, and **NED: 0.0356**. **GRESEL2_run2**, which joined lines, performed better on **BLEU: 0.6949**, **ROUGE-1: 0.8686**, **ROUGE-2: 0.7914**, **ROUGE-L: 0.8626**, and **ROUGE-LSum: 0.8643** [2507.04878]. The immediate significance is that output formatting is not a peripheral implementation detail; it changes the apparent ranking of systems depending on the metric family.

The Tesseract results show that fine-tuning did not uniformly improve performance. On the official test set, the baseline was better in **6 of 8 metrics**. The fine-tuned version improved only the character-level measures, reaching **Levenshtein: 89.1427** and **NED: 0.0302**, while the baseline had better **WER: 0.3650 vs. 0.3846**, **BLEU: 0.6229 vs. 0.6220**, **ROUGE-1: 0.8306 vs. 0.8232**, **ROUGE-2: 0.7114 vs. 0.6907**, **ROUGE-L: 0.8256 vs. 0.8180**, and **ROUGE-LSum: 0.8299 vs. 0.8226**. The paper interprets this as a likely indication of **overfitting** or of limited usefulness of the fine-tuning setup on a highly heterogeneous historical corpus.

The Granite system was the strongest overall submission from the study in many respects. **GRESEL2_run1** achieved **Levenshtein: 97.2399**, **WER: 0.2643**, **NED: 0.0330**, **BLEU: 0.6890**, **ROUGE-1: 0.8841**, **ROUGE-2: 0.8049**, **ROUGE-L: 0.8806**, and **ROUGE-LSum: 0.8837**. According to the report, this corresponded to **2nd place** in **WER** and all **ROUGE** metrics, and **3rd place** in **BLEU**, while remaining weaker on character-level accuracy than the best Tesseract or Transkribus scores.

The qualitative error analysis is equally important. Both Tesseract variants struggled especially with pages that contained **almost no text**, had **low contrast**, had **diagram-like or unusual layouts**, or included **decorative elements**. Granite showed a different profile: it tended to **join hyphenated line breaks**, sometimes **“corrected” historical spellings** into modern Spanish forms, and could **hallucinate** when page regions were unclear. It handled blank pages safely by not generating text, ignored a stamp reading **“BIBLIOTECA NACIONAL”** in one example while correctly recognizing a drop cap, and failed badly on a **two-column page**, where it ignored one column and hallucinated menu items and portion sizes. These observations indicate that multimodal OCR and classical OCR fail for different reasons, and that PastReader exposes both literal-recognition errors and higher-level document-understanding errors.

## 6. Practical implications, computational considerations, and future work

PastReader, as instantiated in the GRESEL report, demonstrates three broader points. First, the benchmark is a **realistic comparison space across OCR paradigms**: a specialized archival OCR platform, a standard OCR engine, and a compact vision-language model can all be evaluated under the same historical-document constraints. Second, **line-break handling** has direct consequences for evaluation, making formatting strategy part of system design rather than post-processing. Third, a **small multimodal model** can be competitive with classical OCR tools on historical Spanish material even under severe hardware constraints [2507.04878].

The computational profile reinforces this distinction. **Tesseract** is reported as much more efficient than the fine-tuned multimodal approach. **Granite** fine-tuning and inference were much more costly computationally. **Transkribus** emissions could not be precisely quantified because the platform is cloud-based, though the platform estimated around **3 hours of processing time**. This suggests that PastReader is not only a question of recognition quality, but also of deployment regime, energy cost, and access to hardware.

The future-work agenda in the paper is correspondingly hybrid. The authors propose creating a **dataset variant with transcription guidelines** grounded in philology and paleography, testing **other compact multimodal models**, trying **larger models**, and fine-tuning with **better hardware/cloud resources** to avoid aggressive image downsampling. In collaboration with **Biblioteca Nacional de España (BNE)**, they also plan to continue exploring techniques using the Spanish-language dataset provided by the shared task. Within that trajectory, PastReader emerges as a benchmark for historical Spanish OCR that binds together document-image degradation, editorial fidelity, model behavior under layout complexity, and the methodological problem of how transcription quality should be measured.

Source: https://www.emergentmind.com/topics/pastreader