<title>Font identification using the grating cell texture operator</title>

Large-Scale Font Identification from Document Images

Lecture Notes in Computer Science - Pattern Recognition ◽

10.1007/978-3-030-41299-9_46 ◽

2020 ◽

pp. 594-600

Author(s):

Subhankar Ghosh ◽

Prasun Roy ◽

Saumik Bhattacharya ◽

Umapada Pal

Keyword(s):

Large Scale ◽

Document Images ◽

Font Identification

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HIDDEN MARKOV MODELS IN TEXT RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001495000389 ◽

1995 ◽

Vol 09 (06) ◽

pp. 925-958 ◽

Cited By ~ 11

Author(s):

J.C. ANIGBOGU ◽

A. BELAÏD

Keyword(s):

Hidden Markov Models ◽

Character Recognition ◽

Markov Models ◽

Hidden Markov ◽

Majority Vote ◽

The Other ◽

Distance Measures ◽

Original Form ◽

Verification Methods ◽

Font Identification

A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.

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