AI-ASSISTED PIPELINE FOR SEMI-AUTOMATIC CONSTRUCTION OF HTR CORPORA FROM HISTORICAL DOCUMENTS

Abstract

This paper presents an improved software application for the semi-automatic annotation of handwritten words from historical documents using a CRNN deep learning model. The proposed approach integrates an artificial intelligence module directly into the annotation process, automatically generating a preliminary transcription for each image. It implements the Human-in-the-Loop concept, where the prediction serves as a recommendation that can be accepted, corrected, or replaced by the annotator, preserving expert control over the final annotation while reducing repetitive manual input.

The application provides automated ordering of images according to metadata, structured storage of transcriptions and document information, text normalization, detection of already processed samples, and resumption of interrupted annotation sessions. The AI prediction module is integrated directly into the graphical interface, allowing the annotator to review and edit the generated transcription without switching between separate tools.

The CRNN model was trained on a custom corpus of 2,763 segmented handwritten word images with diverse graphical and linguistic characteristics. Training employed image augmentation, AdamW optimization, CTC loss, Gradient Clipping, adaptive learning-rate reduction, and Early Stopping. The main focus of the study is the evaluation of annotation performance and the effect of AI-assisted predictions on user productivity.

Experimental results showed that the average processing time decreased from 8.4 s per word with manual transcription to 4.7 s when an AI prediction was available. This corresponds to a 1.79-fold increase in processing speed and a 44.0% reduction in processing time. Annotator productivity increased from 7.1 to 12.8 words per minute. These results demonstrate the practical effectiveness of integrating HTR prediction into an expert annotation workflow and its potential to accelerate the construction of training corpora for historical handwritten text recognition systems while maintaining expert verification of the final annotations.