# Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition

arXiv: https://arxiv.org/abs/1807.02004 · HTML: https://mlsanity.com/paper/1807.02004/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 15 of 15)

- [Optical Character Recognition of 19th Century Classical Commentaries: the Current State of Affairs](https://mlsanity.com/paper/2110.06817/) (2021)
- [Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning](https://mlsanity.com/paper/2106.07881/) (2021)
- [OCR Improves Machine Translation for Low-Resource Languages](https://mlsanity.com/paper/2202.13274/) (2022)
- [Open Source Handwritten Text Recognition on Medieval Manuscripts using Mixed Models and Document-Specific Finetuning](https://mlsanity.com/paper/2201.07661/) (2022)
- [A Comprehensive Gold Standard and Benchmark for Comics Text Detection and Recognition](https://mlsanity.com/paper/2212.14674/) (2022)
- [Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting](https://mlsanity.com/paper/2207.06694/) (2022)
- [Invisible Threats: Backdoor Attack in OCR Systems](https://mlsanity.com/paper/2310.08259/) (2023)
- [TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction](https://mlsanity.com/paper/2604.22880/) (2026)
- [When Low CER is Not Enough: An Analysis of Hallucinations in Vision-Language OCR Systems on Historical Uruguayan Documents](https://mlsanity.com/paper/2607.24077/) (2026)
- [VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extraction](https://mlsanity.com/paper/2510.10490/) (2025)
- [Stringalign: Moving beyond summary statistics with a transparent Unicode-aware tool for evaluating automatic transcription models](https://mlsanity.com/paper/2606.16015/) (2026)
- [LV-ROVER-MLT: Low-Resource Maltese OCR by Synthetic Fine-Tuning and Multi-Stream Arbitration](https://mlsanity.com/paper/2607.00250/) (2026)
- [Conversational Retrieval and On-the-Fly Knowledge Modeling of Historical Penitentiary Repression Records](https://mlsanity.com/paper/2607.08459/) (2026)
- [DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth](https://mlsanity.com/paper/2607.16203/) (2026)
- [A Free Lunch? Adapting PP-OCRv6 for Historical Text Recognition](https://mlsanity.com/paper/2609.20064/) (2026)

## Abstract (arXiv)

Optical Character Recognition (OCR) on contemporary and historical data is still in the focus of many researchers. Especially historical prints require book specific trained OCR models to achieve applicable results (Springmann and L"udeling, 2016, Reul et al., 2017a). To reduce the human effort for manually annotating ground truth (GT) various techniques such as voting and pretraining have shown to be very efficient (Reul et al., 2018a, Reul et al., 2018b). Calamari is a new open source OCR line recognition software that both uses state-of-the art Deep Neural Networks (DNNs) implemented in Tensorflow and giving native support for techniques such as pretraining and voting. The customizable network architectures constructed of Convolutional Neural Networks (CNNS) and Long-ShortTerm-Memory (LSTM) layers are trained by the so-called Connectionist Temporal Classification (CTC) algorithm of Graves et al. (2006). Optional usage of a GPU drastically reduces the computation times for both training and prediction. We use two different datasets to compare the performance of Calamari to OCRopy, OCRopus3, and Tesseract 4. Calamari reaches a Character Error Rate (CER) of 0.11% on the UW3 dataset written in modern English and 0.18% on the DTA19 dataset written in German Fraktur, which considerably outperforms the results of the existing softwares.