Formalization and Initial Experimental Evaluationof an Adaptive Approachto OCR Pipeline Selection for Text RecognitioninImages
DOI:
https://doi.org/10.15407/fmmit2026.42.158Keywords:
оптичне розпізнавання символів, OCR, попередня обробка зображень, Tesseract, EasyOCR, PaddleOCR, RapidOCR, AmazonTextract, CER, WER, інтегральна оц інкаAbstract
The paper addresses the problem of selecting an appropriate text recognition
pipeline for images by considering image preprocessing methods and the specific
features of modern optical c haracter recognition (OCR) models. The relevance of the
study is determined by the fact that OCR quality depends not only on the selected
recognition model but also on the characteristics of the input image, including noise,
contrast, illumination, resolut ion, text skew, and background complexity.
The aim of the paper is to formalize an adaptive approach to OCR pipeline
selection and to perform its initial experimental evaluation. The proposed approach is
based on generating several preprocessed versions of the same input image, applying
OCR models to each version, obtaining recognized text, text region coordinates,
confidence scores, and processing time, and then evaluating the obtained results using
a multi criteria quality score. The study considers the following OCR tools: Tesseract,
EasyOCR, PaddleOCR, RapidOCR, and Amazon Textract. The preprocessing
configurations include the original image without preprocessing, grayscale
conversion, contrast enhancement, denoising with scaling, and Otsu binarization. The
quality assessment is based on Character Error Rate (CER), Word Error Rate (WER),
processing time, model confidence score, and fuzzy matching score. The experimental
part is considered as an initial experimental evaluation rather than a full scale
sta tistical comparison of OCR models. Its purpose is to verify the logic of the proposed
methodology, identify the main parameters that should be fixed in further experiments,
and prepare a basis for extended research on a larger dataset of images of different
quality. The obtained results demonstrate that the quality of OCR recognition may vary
depending on the selected combination of preprocessing method and OCR model.
However, the results should be interpreted as preliminary and cannot be considered a
final ranking of OCR models. The practical value of the proposed approach lies in its
potential use as a methodological basis for building OCR pipelines in automated
document processing systems, digital archives, electronic document management
systems, information retrieval systems, and applications for text recognition from
images.
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Copyright (c) 2026 Христина Грицай, Оксана Грицай, Ольга Терендій (Автор)
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