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The paper discusses an approach to decipher large collections of handwrittenindex cards of historical dictionaries. Our study provides a working solutionthat reads the cards, and links their lemmas to a searchable list of dictionaryentries, for a large historical dictionary entitled the Dictionary of the 17th-and 18th-century Polish, which comprizes 2.8 million index cards. We apply atailored handwritten text recognition (HTR) solution that involves (1) anoptimized detection model; (2) a recognition model to decipher the handwrittencontent, designed as a spatial transformer network (STN) followed byconvolutional neural network (RCNN) with a connectionist temporalclassification layer (CTC), trained using a synthetic set of 500,000 generatedPolish words of different length; (3) a post-processing step using constrainedWord Beam Search (WBC): the predictions were matched against a list ofdictionary entries known in advance. Our model achieved the accuracy of 0.881on the word level, which outperforms the base RCNN model. Within this study weproduced a set of 20,000 manually annotated index cards that can be used forfuture benchmarks and transfer learning HTR applications.

Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets