Using Machine Learning to Recognize and Extract Text from Images.

Playing with text to image library

Optical Character Recognition (OCR) is as old as computers are, there have been numerous techniques to extract text out of digital images. All the way from predefined old-school image processing to deep learning on a cloud. In this article, we look at open source frame tesseract and custom trained model to see how we can perform OCR with high accuracy and Non-English languages.

There is no one size fit all solution when it comes to languages. With a diverse set of languages that we have, it becomes difficult to build a classifier which can identify and extract different characters. Some languages are written right to left and some have similar looking character set.

Tesseract is an open source text recognizer engine which allows you to build models using LSTM deep learning for any language. There are a decent set of models available which are ready to use. I think Tesseract is a good framework to get your hands dirty.

I’ve come across few services which use tesseract to prove high accuracy image to text for Indic Languages, check out

It’s a free service which supports the wide range of languages to convert images to text. It supports the following languages :

AmharicGujaratiPanjabi; Punjabi
ArabicHaitian; Haitian CreolePolish
AzerbaijaniHindiPushto; Pashto
BelarusianCroatianRomanian; Moldavian; Moldovan
BosnianIndonesianSinhala; Sinhalese
Catalan; ValencianItalianSlovak – Fraktur
CzechJapaneseSpanish; Castilian
Chinese – SimplifiedKazakhYiddish
Chinese – TraditionalCentral KhmerAlbanian
CherokeeKirghiz; KyrgyzSerbian
FrankishBurmeseUighur; Uyghur
IrishDutch; FlemishUrdu

APIs are written in Go-Lang and data processing is done using Python and Tensorflow.

Feel free to leave a comment in case you want me to focus on any specific area.

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