Fig. 1. A sample index card with its header detected: the outlined words will next be subject to recognition in next steps of the procedure. The card comes from the Dictionary of the 17th- and 18th-century Polish.

The project aims to decipher large collections of handwritten index cards of historical dictionaries. We provide a working solution that reads the cards, and links their lemmas to a searchable list of dictionary entries, for a large historical dictionary entitled the Dictionary of the 17^th^- and 18^th^-century Polish, which comprizes 2.8 million index cards. We apply a tailored handwritten text recognition (HTR) solution that involves (1) an optimized detection model; (2) a recognition model to decipher the handwritten content, designed as a spatial transformer network (STN) followed by convolutional neural network (RCNN) with a connectionist temporal classification layer (CTC), trained using a synthetic set of 500,000 generated Polish words of different length; (3) a post-processing step using constrained Word Beam Search (WBC): the predictions were matched against a list of dictionary entries known in advance. Our model achieved the accuracy of 0.881 on the word level, which outperforms the base RCNN model. Within this study we produced a set of 20,000 manually annotated index cards that can be used for future benchmarks and transfer learning HTR applications.

Demo application

You can try how it works! Check out the following link:


Idziak, J., Šeļa, A., Woźniak, M., Leśniak, A., Byszuk, J. and Eder, M. (2021). Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets. In: International Conference on Computational Science (ICCS). (Lecture Notes in Computer Science), forthcoming. [Get preprint]