Image Quality Assessment for predicting OCR

The aim of OCR (Optical Character recognition) is to read and transcript the text present in an image.

The most famous commercial OCR software is certainly FineReader from ABBYY company. Omnipage, from Nuance company is also quite famous. For open source OCR, Tesseract (Google) works quite well.

OCR systems works quite well on relatively “new”, black & white, 300dpi document images. Rotation, size and fonts of characters, blur, stains, noise, etc. will decrease the quality of OCR. Most of the time, many preprocessing are applied on images before submitting them to the OCR : deskewing, despeckling, segmentation, etc.

Some researchers are working on predicting OCR by using IQA (Image Quality Assessment). 3 kinds of methods exist : full image reference [1],   reduced reference [2] and without reference [3].

[1] CAPODIFERRO, Licia, JACOVITTI, Giovanni, et DI CLAUDIO, Elio D. Two-Dimensional Approach to Full-Reference Image Quality Assessment Based on Positional Structural Information. Image Processing, IEEE Transactions on, 2012, vol. 21, no 2, p. 505-516.

[2] REHMAN, Abdul et WANG, Zhou. Reduced-reference image quality assessment by structural similarity estimation. Image Processing, IEEE Transactions on, 2012, vol. 21, no 8, p. 3378-3389.

[3] CIANCIO, Alexandre, DA COSTA, ALN Targino, DA SILVA, Eduardo AB, et al. No-reference blur assessment of digital pictures based on multifeature classifiers. Image Processing, IEEE Transactions on, 2011, vol. 20, no 1, p. 64-75.

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