White Papers &

For an in-depth view into Smart Engines’ Research & Development activities we invite you to peruse our latest published work and a sampling from the archives.

February 14, 2020

Two-step CNN framework for text line recognition in camera-captured images

Yulia S. Chernyshova, Alexander V. Sheshkus, Vladimir V. Arlazarov

Publisher: IEEE DOI: 10.1109/ACCESS.2020.2974051

In this paper, we introduce an “on the device” text line recognition framework that is designed for mobile or embedded systems. We consider per-character segmentation as a language-independent problem and individual character recognition as a language-dependent one. Thus, the proposed solution is based on two separate artificial neural networks (ANN) and dynamic programming instead of employing image processing methods for the segmentation step or end-to-end ANN. To satisfy the tight constraints on memory size imposed by embedded systems and to avoid overfitting, we employ ANNs with a small number of trainable parameters. The primary purpose of our framework is the recognition of low-quality images of identity documents with complex backgrounds and a variety of languages and fonts. We demonstrate that our solution shows high recognition accuracy on natural datasets even being trained on purely synthetic data. We use MIDV-500 and Census 1961 Project datasets for text line recognition. The proposed method considerably surpasses the algorithmic method implemented in Tesseract 3.05, the LSTM method (Tesseract 4.00), and unpublished method used in the ABBYY FineReader 15 system. Also, our framework is faster than other compared solutions. We show the language-independence of our segmenter with the experiment with Cyrillic, Armenian, and Chinese text lines.

White Papers

Whitepaper No. 03

Datasets of ID documents: MIDV-500

How to test ID recognition algorithms? $

Whitepaper No. 02

Binarization Algorithms for Documents Recognition

During the 9-year history of International Competition on Document Binarization DIBCO17 held within ICDAR conference, a lot of bold and unconventional algorithms of binarization have been proposed. $

Whitepaper No. 01

ProLAB: Perceptually Uniform Projective Colour Coordinates System

Aiming at advancing the fundamental science, Smart Engines R&D team is closely working with the research group from the Russian Academy of Sciences — specifically the Institute for Information Transmission Problems. $

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