Optical character recognition with neural networks and post-correction with finite state methods

Abstract

The optical character recognition (OCR) quality of the historical part of the Finnish newspaper and journal corpus is rather low for reliable search and scientific research on the OCRed data. The estimated character error rate (CER) of the corpus, achieved with commercial software, is between 8 and 13%. There have been earlier attempts to train high-quality OCR models with open-source software, like Ocropy (https://github.com/tmbdev/ocropy) and Tesseract (https://github.com/tesseract-ocr/tesseract), but so far, none of the methods have managed to successfully train a mixed model that recognizes all of the data in the corpus, which would be essential for an efficient re-OCRing of the corpus. The difficulty lies in the fact that the corpus is printed in the two main languages of Finland (Finnish and Swedish) and in two font families (Blackletter and Antiqua). In this paper, we explore the training of a variety of OCR models with deep neural networks (DNN). First, we find an optimal DNN for our data and, with additional training data, successfully train high-quality mixed-language models. Furthermore, we revisit the effect of confidence voting on the OCR results with different model combinations. Finally, we perform post-correction on the new OCR results and perform error analysis. The results show a significant boost in accuracy, resulting in 1.7% CER on the Finnish and 2.7% CER on the Swedish test set. The greatest accomplishment of the study is the successful training of one mixed language model for the entire corpus and finding a voting setup that further improves the results.

Nykysuomen sanakirjan digitaalinen editio

“…Työn tavoitteisiin liittyvät läheisesti myös jo tunnistetun tekstin korjauksen menetelmät ja niiden tutkimus [13,12]. Tähän Helsingin yliopistossa tehdyn työn jatkumoon sisältyy runsaasti erityisesti historiallisiin teksteihin keskittyvää tutkimusta [10,11]. Aiempi tutkimus ei kuitenkaan ole keskittynyt nimenomaisesti sanakirjojen tekstintunnistamiseen, johon liittyy hyvin erityisiä haasteita.…”

Pattern Separation Network Based on the Hippocampus Activity for Handwritten Recognition

“…Character error rate (CER) was calculated for all the experimental results. CER is the percentage of erroneous characters in the system output and is a common metric in OCR-related tasks [78]. It is the number of erroneous characters divided by the sum of correct characters and errors in the output of the system.…”

OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks

“…In this paper we examine to what extent deep CNN-LSTM hybrid neural networks can improve the character accuracy rate (CAR) on 19th century Swedish newspaper text during recognition. Following Drobac and Lindén (2020) approach we trained a character model for Swedish in Calamari and achieved an average CAR of 97.43% which is a new state-of-the-art result for historical Swedish newspaper text. 2…”

“…To overcome this limitation researchers have been training language specific character recognition models for different time periods (Furrer and Volk, 2011;Breuel et al, 2013;Krishna et al, 2018;Drobac et al, 2019). There have also been attempts to improve the accuracy of the models after recognition by applying post-correction methods (Drobac and Lindén, 2020;Dannélls and Persson, 2020). In this paper we examine to what extent deep CNN-LSTM hybrid neural networks can improve the character accuracy rate (CAR) on 19th century Swedish newspaper text during recognition.…”