DEV Community: Hrishikesh Terdalkar

Google Drive Actions

Hrishikesh Terdalkar — Fri, 30 Dec 2022 20:09:58 +0000

hrishikeshrt / google_drive_actions

Perform common actions using Google Drive API v3

Google Drive API

Simple GoogleDriveApplication class to perform common Google Drive actions such as searching files, downloading entire folders, uploading files, deleting files.

Usage

from application import GoogleDriveApplication

GD = GoogleDriveApplication("path-to-client-secret.json")

# Find files
GD.find_files("hello+world", is_folder=False)

# List entire subtree of the specified folder
GD.list_folder("google-drive-id-of-folder", recursive=True)

# Download entire folder
GD.download_folder("google-drive-id-of-folder")

Create a project on Google Cloud Platform

Wizard: https://console.developers.google.com/start/api?id=drive

Instructions:

https://cloud.google.com/genomics/downloading-credentials-for-api-access
Select application type as "Installed Application"
Create credentials OAuth consent screen --> OAuth client ID
Save client_secret.json

References

View on GitHub

Google Drive Actions (API v3)

Simple GoogleDriveApplication class to perform common Google Drive actions such as searching files, downloading entire folders, uploading files, deleting files.

Usage

from application import GoogleDriveApplication

GD = GoogleDriveApplication("path-to-client-secret.json")

# Find files
GD.find_files("hello+world", is_folder=False)

# List entire subtree of the specified folder
GD.list_folder("google-drive-id-of-folder", recursive=True)

# Download entire folder
GD.download_folder("google-drive-id-of-folder")

Devanagari Transliteration Pipeline for LaTeX

Hrishikesh Terdalkar — Wed, 13 Apr 2022 18:59:18 +0000

hrishikeshrt / devanagari-transliteration-latex

Devanagari Transliteration in LaTeX -- Write in Devanagari to render as IAST, Harvard-Kyoto, Velthuis, SLP1, WX etc.

Devanagari Transliteration in LaTeX

Write in Devanagari to render as IAST, Harvard-Kyoto, Velthuis, SLP1, WX etc.

Devanagari text can be transliterated in various standard schemes. There exist several input systems based on these transliteration schemes to enable users easily input the text. More often than not, a user has a preference of scheme to type the input in. Similarly, at times, one faces a need to render it in a different scheme in the PDF document.

In my case, I prefer using ibus-m17n to type text in Devanagari. While writing articles that contain Devanagari text, I also faced the need to render the text as IAST in the final PDF One could always learn to input text in another input scheme, but that may get tedious. Similarly, transliterating each word using online systems such as Aksharamukha can also be a tedious task. So, I was looking for a way…

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Devanagari is the fourth most widely adopted writing system in the world, primarily used in the Indian subcontinent. The script is being used for more than 120 languages, some of the more notable languages being, Sanskrit, Hindi, Marathi, Pali, Nepali and several variations of these languages.

Devanagari text can be transliterated in various standard schemes. There exist several input systems based on these transliteration schemes to enable users easily input the text. More often than not, a user has a preference of scheme to type the input in. Similarly, at times, one faces a need to render it in a different scheme in the PDF document.

In my case, I prefer using ibus-m17n to type text in Devanagari. While writing articles that contain Devanagari text, I also faced the need to render the text as IAST in the final PDF.
One could always learn to input text in another input scheme, but that may get tedious. Similarly, transliterating each word using online systems such as Aksharamukha can also be a tedious task. So, I was looking for a way where I can type in Devanagari, and have it rendered in IAST after PDF compilation. As a solution, I came up with a system consisting of a small set of LaTeX commands to add custom syntax to LaTeX and a python transliteration script (based on indic-transliteration package) to serve as a middle-layer and process the LaTeX file to create a new LaTeX file with proper transliteration.

LaTeX Compilation System with Transliteration Support

There are two primary components to the system,

LaTeX Synatx
Transliteration Script

LaTeX Syntax

XeTeX (xelatex) and LuaTeX (lualatex) have good unicode support and can be used to write Devanagari text. In the current example, I mention the setup with XeTeX.

We first add the required packages in the preamble of the LaTeX (.tex) file.

% This assumes your files are encoded as UTF8
\usepackage[utf8]{inputenc}

% Devanagari Related Packages
\usepackage{fontspec, xunicode, xltxtra}

Using fontspec, we can define environments for font families, to write text in specific scripts. To write Devanagari text, one needs to have a Devanagari font available. (It is assumed here that one may need to write both in Devanagari as well as other transliteration schemes.)

For more on Devanagari fonts, you may check the fonts section of this document. In this section, it is assumed that Sanskrit 2003 font is installed in the system.

To define the environments as mentioned earlier, we add the following lines in the preamble.

% Define Fonts
\newfontfamily\textskt[Script=Devanagari]{Sanskrit 2003}
\newfontfamily\textiast[Script=Latin]{Sanskrit 2003}

% Commands for Devanagari Transliterations
\newcommand{\skt}[1]{{\textskt{#1}}}
\newcommand{\iast}[1]{{\textiast{#1}}}
\newcommand{\Iast}[1]{{\textiast{#1}}}
\newcommand{\IAST}[1]{{\textiast{#1}}}

This provides us with four commands. \skt{} can be used to render Devanagari text. \iast{}, \Iast{} and \IAST{} can be used to render devanagari text in IAST format in lower case, title case and upper case respectively. It should be noted that from the perspective of LaTeX engine, the commands \iast{}, \Iast{} and \IAST{} are identical. They are just different syntactically to aid the python script to perform transliteration and apply appropriate modifications.
It should further be noted that we can define new font families and new commands for any of the valid schemes as per the requirement, which can potentially give us additional commands such \velthuis{}, \hk{} and so on.

Minimal Example

Equipped with these commands, and some Devanagari text, we have a minimal example as follows, stored in the file minimal.tex,

\documentclass[10pt]{article}

% This assumes your files are encoded as UTF8
\usepackage[utf8]{inputenc}

% Devanagari Related Packages
\usepackage{fontspec, xunicode, xltxtra}

% Define Fonts
\newfontfamily\textskt[Script=Devanagari]{Sanskrit 2003}
\newfontfamily\textiast[Script=Latin]{Sanskrit 2003}

% Commands for Devanagari Transliterations
\newcommand{\skt}[1]{{\textskt{#1}}}
\newcommand{\iast}[1]{{\textiast{#1}}}
\newcommand{\Iast}[1]{{\textiast{#1}}}
\newcommand{\IAST}[1]{{\textiast{#1}}}

\title{Transliteration of Devanagari Text}
\author{Hrishikesh Terdalkar}

\begin{document}

\maketitle

\skt{को न्वस्मिन् साम्प्रतं लोके गुणवान् कश्च वीर्यवान्।}

\iast{को न्वस्मिन् साम्प्रतं लोके गुणवान् कश्च वीर्यवान्।}

\Iast{को न्वस्मिन् साम्प्रतं लोके गुणवान् कश्च वीर्यवान्।}

\IAST{को न्वस्मिन् साम्प्रतं लोके गुणवान् कश्च वीर्यवान्।}

\end{document}

Transliteration Script

The python script is used to perform transliteration and some clean-up on the LaTeX.

python3 finalize.py minimal.tex final.tex

This result in the content being transformed in the following way,

% ...

\skt{को न्वस्मिन् साम्प्रतं लोके गुणवान् कश्च वीर्यवान्।}

\iast{ko nvasmin sāmprataṃ loke guṇavān kaśca vīryavān|}

\Iast{Ko Nvasmin Sāmprataṃ Loke Guṇavān Kaśca Vīryavān|}

\IAST{KO NVASMIN SĀMPRATAṂ LOKE GUṆAVĀN KAŚCA VĪRYAVĀN|}

% ...

We can now proceed to compile the final.tex file.

xelatex final

This results in the following output,

Anatomy of the Transliteration Script

At the core of the transliteration script, there is a function transliterate_between.

def transliterate_between(
    text: str,
    from_scheme: str,
    to_scheme: str,
    start_pattern: str,
    end_pattern: str,
    post_hook: Callable[[str], str] = lambda x: x,
) -> str:
    """Transliterate the text appearing between two patterns

    Only the text appearing between patterns `start_pattern` and `end_pattern`
    it transliterated.
    `start_pattern` and `end_pattern` can appear multiple times in the full
    text, and for every occurrence, the text between them is transliterated.

    `from_scheme` and `to_scheme` should be compatible with scheme names from
    `indic-transliteration`

    Parameters
    ----------
    text : str
        Full text
    from_scheme : str
        Input transliteration scheme
    to_scheme : str
        Output transliteration scheme
    start_pattern : regexp
        Pattern describing the start tag
    end_pattern : regexp
        Pattern describing the end tag
    post_hook : Callable[[str], str], optional
        Function to be applied on the text within tags after transliteration
        The default is `lambda x: x`.

    Returns
    -------
    str
        Text after replacements
    """

    if from_scheme == to_scheme:
        return text

    def transliterate_match(matchobj):
        target = matchobj.group(1)
        replacement = transliterate(target, from_scheme, to_scheme)
        replacement = post_hook(replacement)
        return f"{start_pattern}{replacement}{end_pattern}"

    pattern = "%s(.*?)%s" % (re.escape(start_pattern), re.escape(end_pattern))
    return re.sub(pattern, transliterate_match, text, flags=re.DOTALL)

We can provide the start and end patterns as \iast{ and } respsectively, to transliterate the text enclosed in these tags.

Using this function, we can write a generic function to work with any transliteration scheme.

def latex_transliteration(
    input_text: str,
    from_scheme: str,
    to_scheme: str
) -> str:
    """Transliaterate parts of the LaTeX input enclosed in scheme tags

    A scheme tag is of the form `\\to_scheme_lowercase{}` and is used
    when the desired output is in `to_scheme`.

    i.e.,
    - Tags for IAST scheme are enclosed in \\iast{} tags
    - Tags for VH scheme are enclosed in \\vh{} tags
    - ...

    Parameters
    ----------
    input_text : str
        Input text
    from_scheme : str
        Transliteration scheme of the text written within the input tags
    to_scheme : str
        Transliteration scheme to which the text within tags should be
        transliterated

    Returns
    -------
    str
        Text after replacement of text within the scheme tags
    """
    start_tag_pattern = f"\\{to_scheme.lower()}"
    end_tag_pattern = "}"
    return transliterate_between(
        input_text,
        from_scheme=from_scheme,
        to_scheme=to_scheme,
        start_pattern=start_tag_pattern,
        end_pattern=end_tag_pattern
    )

Note: The names of schemes (and therefore the corresponding LaTeX commands) have to conform to the names of schemes used
by the indic-transliteration package.

IAST is a case-insensitive transliteration scheme, and as such, we might be interested in specific capitalization of certain words (e.g. proper nouns). We can use the post_hook argument to provide this function. Using that, we can create a function to handle the three variants of IAST mentioned previously, namely, \iast{} (lower), \Iast{} (title) and \IAST{} (upper).

def devanagari_to_iast(input_text: str) -> str:
    """Transliaterate parts of the input enclosed in
    \\iast{}, \\Iast{} or \\IAST{} tags from Devanagari to IAST

    Text in \\Iast{} tags also undergoes a `.title()` post-hook.
    Text in \\IAST{} tags also undergoes a `.upper()` post-hook.

    Parameters
    ----------
    input_text : str
        Input text

    Returns
    -------
    str
        Text after replacement of text within the IAST tags
    """
    intermediate_text = transliterate_between(
        input_text,
        from_scheme=sanscript.DEVANAGARI,
        to_scheme=sanscript.IAST,
        start_pattern="\\iast{",
        end_pattern="}"
    )
    intermediate_text = transliterate_between(
        intermediate_text,
        from_scheme=sanscript.DEVANAGARI,
        to_scheme=sanscript.IAST,
        start_pattern="\\Iast{",
        end_pattern="}",
        post_hook=lambda x: x.title()
    )
    final_text = transliterate_between(
        intermediate_text,
        from_scheme=sanscript.DEVANAGARI,
        to_scheme=sanscript.IAST,
        start_pattern="\\IAST{",
        end_pattern="}",
        post_hook=lambda x: x.upper()
    )

    return final_text

Finally, there are other utility functions to remove comments and clean excessive whitespaces.

Extras

Additionally, we may want some more structure to our setup, such as,

Separation of ontent into multiple files

\input{sections/section_devanagari.tex}
\input{sections/section_iast_lower.tex}
\input{sections/section_iast_title.tex}
\input{sections/section_iast_upper.tex}

Bibliography

\bibliographystyle{acm}
\bibliography{papers}

Final LaTeX Preparation

We may have used the scheme tags across multiple sections. One option is to apply the transliteration script on every section file, to create a new set of section files and use those to compile the final LaTeX file.

A simpler solution is available in the form of latexpand which resolves the \input{} commands to actually include the content and create a single consolidated LaTeX file.

latexpand main.tex > single.tex

Now, we can run the python script on this file to resolve the transliteration tags.

python3 finalize.py main.tex final.tex

Compilation

When working with BibTeX, we often need to multiple times to get the correct rendering of references in the PDF. Usually, this requires

xelatex final
bibtex final
xelatex final
xelatex final

Alternatively, we can use latexmk which takes care of the tedious compilation routines and reduces our job to a single command,

latexmk -pdflatex='xelatex %O %S' -pdf -ps- -dvi- final.tex

Another benefit of using latexmk is, we can clean the numerous files generated by LaTeX engine using a one-liner as well,

latexmk -c

Makefile

Finally, we can place all of the console commands together in a Makefile.

all: .all

.all: main.tex sections/*.tex papers.bib
        latexpand main.tex > single.tex
        python3 finalize.py single.tex final.tex

        latexmk -pdflatex='xelatex %O %S' -pdf -ps- -dvi- final.tex

clear:
        latexmk -C
        rm single.tex
        rm final.tex

clean:
        latexmk -c

Thus, now we can focus on writing content in the .tex files and once we are done, simply use the command,

make

Requirements

We have made use of a number of external tools, and it is required to have these setup prior to the described solution.

Minimal Requirements

The minimal example mentioned earlier requires only three things,

XeLaTeX (unicode support) (included in TeX Live)
Python3
indic-transliteration

Extra Requirements

The extras have some more dependencies.

BibTeX (optional) (bibliography support)
latexpand (optional) (resolve \input{})
latexmk (optional) (simpler TeX compilation)

Devanagari Fonts

Nowadays, there are several good Devanagari fonts available. Google Fonts also provides a wide variety of Devanagari fonts.

Two of my personal favourites are,

Code

The source code for the entire setup is available at hrishikeshrt/devanagari-transliteration-latex.

pyopendota: Python Package for OPENDOTA API

Hrishikesh Terdalkar — Fri, 04 Mar 2022 09:33:38 +0000

hrishikeshrt / pyopendota

Python wrapper for <OPENDOTA/> API

<OPENDOTA/> API

A python interface for <OPENDOTA/> API

The OpenDota class provided with the package serves as a python interface for the original OpenDota API in the form of a thin wrapper The class assumes some familiarity with the OpenDota API.

All method calls return serializable python objects, as return by the API in most cases a dict or a list. Response data is stored as JSON in a local directory (Default: ~/dota2), to prevent the load on OpenDota API.

Free software: MIT license
Documentation: https://pyopendota.readthedocs.io.

Features

Transparent wrapper for majority of the API calls
Ability to authenticate using API key
In-built and cusomizable limit to protect against frequent API calls
Local file-based storage for frequent requests
Basic CLI using fire

Usage

Use <OPENDOTA/> API in a project

import opendota

# Initialize the API-connection object
client = opendota.OpenDota()

Get Common Entities

client.get_matches(

…

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About OpenDota API

The OpenDota API provides Dota 2 related data including advanced match data extracted from match replays. OpenDota API Documentation: https://docs.opendota.com/

About `pyopendota`

The OpenDota class provided with the package serves as a python interface for the original OpenDota API in the form of a thin wrapper. The class assumes some familiarity with the OpenDota API.

All method calls return serializable python objects, as return by the API, in most cases a dict or a list. Response data is stored as JSON in a local directory (Default: ~/dota2), to prevent the load on OpenDota API.

Features

Transparent wrapper for majority of the API calls
Ability to authenticate using API key
In-built and cusomizable limit to protect against frequent API calls
Local file-based storage for frequent requests

Extra

Apart from the direct API calls, it also lets one "Search by name" for pro players, teams, leagues etc.

Py-Stopwatch: Measure execution time of your python code segments

Hrishikesh Terdalkar — Fri, 04 Mar 2022 08:38:45 +0000

hrishikeshrt / py_stopwatch

Python Stopwatch Class

py-stopwatch

Stopwatch class for timing portions of python code.

Free software: MIT license
Documentation: https://py-stopwatch.readthedocs.io.

Features

Tick-based stopwatch
Support for Pause/Resume
Support for multiple named-ticks
Utility functions for time between different ticks
No third party requirements.

Usage

from stopwatch import Stopwatch
t = Stopwatch()
t.start()
print("Started ..")
time.sleep(0.24)
print(f"t.tick(): {t.tick():.4f} seconds")
time.sleep(0.48)
print(f"t.tick(): {t.tick():.4f} seconds")
time.sleep(0.16)
print(f"t.tick('Named Tick-1'): {t.tick('Named Tick-1'):.4f} seconds")
t.pause()
print("Paused ..")
time.sleep(0.12)
t.resume()
print("Resumed ..")
print(f"t.last(): {t.last():.4f} seconds")
time.sleep(0.12

…

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Features

Tick-based stopwatch.
Support for Pause / Resume.
Support for multiple named-ticks.
Utility functions for time between different ticks.
No third party requirements.

Usage


from stopwatch import Stopwatch
t = Stopwatch()
t.start()
print("Started ..")
time.sleep(0.24)
print(f"t.tick(): {t.tick():.4f} seconds")
time.sleep(0.48)
print(f"t.tick(): {t.tick():.4f} seconds")
time.sleep(0.16)
print(f"t.tick('Named Tick-1'): {t.tick('Named Tick-1'):.4f} seconds")
t.pause()
print("Paused ..")
time.sleep(0.12)
t.resume()
print("Resumed ..")
print(f"t.last(): {t.last():.4f} seconds")
time.sleep(0.12)
print(f"t.tick(): {t.tick():.4f} seconds")
time.sleep(0.12)
print(f"t.tick('Named Tick-2'): {t.tick('Named Tick-2'):.4f} seconds")
t.stop()
print("Timer stopped.")
print("---")
print(f"Total pause: {t.time_paused:.2f} seconds.")
print(f"Total runtime: {t.time_active:.2f} seconds.")
print(f"Total time: {t.time_total:.2f} seconds.")
tij = t.time_elapsed(start_name='Named Tick-1', end_name='Named Tick-2')
print(f"Time between 'Named Tick-1' and 'Named Tick-2': {tij:.4f}")

Elegant WebApp Template using Flask + Bootstrap + PythonAnywhere

Hrishikesh Terdalkar — Thu, 03 Mar 2022 09:40:21 +0000

hrishikeshrt / flask-bootstrap-anywhere

Quickly set up a web-based application using Flask styled with Bootstrap and deploy on PythonAnywhere

Flask-Bootstrap-Anywhere

This is a skeleton application written in python using Flask microframework It comes with support for user registration, login, session management password recovery, password reset and database connection. These features are achieved by Flask-Security-Too, which in turn achieves them by various flask extensions such as Flask-Login, Flask-WTF etc. Front-end styling is done using Bootstrap.

Motivation

In general, many web applications require some level of user-management. While Django can be suitable for big projects, for relatively small projects it might be an overkill. Further, Flask offers more flexibility. However, with Flask being a "microframework", we have to choose from a variety of plugins to build a working system.

This application fits together a handful of such commonly used plugins for commonly required tasks, and aims to save developers' effort.

Features

Back-end

User Management
- Registration
- Login
- Session Management
- Password Recovery
- Roles
Database Support
- MySQL
- SQLite
- MongoDB (incomplete)
Mail
…

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Motivation

In general, many web applications require some level of user-management.
While Django can be suitable for big projects, for relatively small projects
it might be an overkill. Further, Flask offers more flexibility.
However, with Flask being a "microframework", we have to choose from a variety
of plugins to build a working system.

This application fits together a handful of such commonly used plugins
for commonly required tasks, and aims to save developers' effort.

Features

Back-end

User Management
- Registration
- Login
- Session Management
- Password Recovery
- Roles
Database Support
- MySQL
- SQLite
Mail
Migrations (Powered by Flask-Migrate)
Deployment
- Support for Git-powered release on on PythonAnywhere

Front-end

Bootstrap-powered clean front-end
Elegant themes powered by https://bootswatch.com/
Ability to change themes on the fly

Wordle CLI

Hrishikesh Terdalkar — Thu, 03 Mar 2022 09:30:37 +0000

hrishikeshrt / python-wordle

Wordle in Python -- Game, Solver and Helper! Play Wordle on the terminal. Optionally, let WordleSolver play it out for you! Use WordleSolver's help for solving your Wordles provided by other platforms.

Wordle

Wordle -- Game, Solver and Helper!

Free software: GNU General Public License v3
Documentation: https://python-wordle.readthedocs.io.

Features

Comprehensive CLI Application
- Play Wordle in your terminal!
- Let the solver play it out for you!
- Use the helper mode for solving Wordles provided by other platforms!
New Wordle every day.
- Disclaimer: This has nothing to do with the NYTimes Wordle.
Play more using --random option.
Reproducible random Wordle by specifying --seed.
WordleSolver() class for solving Wordle.
--solve option to let WordleSolver() do its work in front of your eyes!
--helper option to activate the helper mode for help with solving Wordles provided by other platforms.

Install

To install, run this command in your terminal:

$ pip install python-wordle

Usage

usage: wordle [-h] [--random] [--seed SEED] [--solve] [--helper]
Wordle on your terminal

optional arguments:
-h, --help   show this help message and exit
--random     Show a random Wordle
--seed SEED  Seed

…

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python-wordle is a Wordle toolkit containing the game, a solver and a helper!

Example

Features

Comprehensive CLI Application
- Play Wordle in your terminal!
- Let the solver play it out for you!
- Use the helper mode for solving Wordles provided by other platforms!
New Wordle every day.
- Disclaimer: This has nothing to do with the NYTimes Wordle.
Play more using --random option.
Reproducible random Wordle by specifying --seed.
WordleSolver() class for solving Wordle.
--solve option to let WordleSolver() do its work in front of your eyes!
--helper option to activate the helper mode for help with solving Wordles provided by other platforms.

Install

To install, run this command in your terminal:

pip install python-wordle

Usage


usage: wordle [-h] [--random] [--seed SEED] [--solve] [--helper]

Wordle on your terminal

optional arguments:
  -h, --help   show this help message and exit
  --random     Show a random Wordle
  --seed SEED  Seed the RNG
  --solve      Simulate WordleSolver
  --helper     Take help from WordleSolver for a Wordle in a different platform

PyCDSL - Python Interface to Cologne Digital Sanskrit Lexicon (CDSL)

Hrishikesh Terdalkar — Thu, 03 Mar 2022 09:22:52 +0000

hrishikeshrt / PyCDSL

Python Interface to Cologne Digital Sanskrit Lexicon (CDSL)

PyCDSL

PyCDSL is a python interface to Cologne Digital Sanskrit Lexicon (CDSL).

Free software: GNU General Public License v3
Documentation: https://pycdsl.readthedocs.io.

Features

CDSL Corpus Management (Download, Update, Access)
Unified Programmable Interface to access all dictionaries available at CDSL
Console Command and REPL Interface for easy dictionary search
Extensive support for transliteration using indic-transliteration module

Install

To install PyCDSL, run this command in your terminal:

$ pip install PyCDSL

Usage

PyCDSL can be used in a python project, as a console command and as an interactive REPL interface.

Using PyCDSL in a Project

Import PyCDSL in a project:

import pycdsl

Create a CDSLCorpus Instance:

# Default installation at ~/cdsl_data
CDSL = pycdsl.CDSLCorpus()
# Custom installation path can be specified with argument `data_dir`
# e.g. CDSL = pycdsl.CDSLCorpus(data_dir="custom-installation-path")

# Custom transliteration schemes for input and output can be specified
# with arguments `input_scheme` and `output_scheme`.
# Values

…

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Perform OCR using Google's Drive API v3

Hrishikesh Terdalkar — Thu, 03 Mar 2022 09:20:37 +0000

hrishikeshrt / google_drive_ocr

Perform OCR using Google's Drive API v3

Google OCR (Drive API v3)

Perform OCR using Google's Drive API v3

Free software: GNU General Public License v3
Documentation: https://google-drive-ocr.readthedocs.io.

Features

Perform OCR using Google's Drive API v3
Class GoogleOCRApplication() for use in projects
Highly configurable CLI
Run OCR on a single image file
Run OCR on multiple image files
Run OCR on all images in directory
Use multiple workers (multiprocessing)
Work on a PDF document directly

Usage

Using in a Project

Create a GoogleOCRApplication application instance:

from google_drive_ocr import GoogleOCRApplication

app = GoogleOCRApplication('client_secret.json')

Perform OCR on a single image:

app.perform_ocr('image.png')

Perform OCR on mupltiple images:

app.perform_batch_ocr(['image_1.png', 'image_2.png', 'image_3.png'])

Perform OCR on multiple images using multiple workers (multiprocessing):

app.perform_batch_ocr(['image_1.png', 'image_3.png', 'image_2.png'], workers=2)

Using Command Line Interface

Typical usage with…

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Google's Drive API can be used to perform OCR on images from any language. google-drive-ocr is a python package that allows users to do this with utmost ease, right from the terminal.

Free software: GNU General Public License v3
Documentation: https://google-drive-ocr.readthedocs.io.

Features

Perform OCR using Google's Drive API v3
Class GoogleOCRApplication() for use in projects
Highly configurable CLI
Run OCR on a single image file
Run OCR on multiple image files
Run OCR on all images in directory
Use multiple workers (multiprocessing)
Work on a PDF document directly

Install

To install Google OCR (Drive API v3), run this command in your terminal:

pip install google-drive-ocr

Note: One must setup a Google application and download client_secrets.json file before using google_drive_ocr.

Setup

Create a project on Google Cloud Platform
Wizard: https://console.developers.google.com/start/api?id=drive

Instructions

https://cloud.google.com/genomics/downloading-credentials-for-api-access
Select application type as "Installed Application"
Create credentials "OAuth consent screen" --> "OAuth client ID"
Save client_secret.json

Usage

Using in a Project

Create a GoogleOCRApplication application instance:

from google_drive_ocr import GoogleOCRApplication

app = GoogleOCRApplication('client_secret.json')

Perform OCR on a single image:

app.perform_ocr('image.png')

Perform OCR on mupltiple images:

app.perform_batch_ocr(['image_1.png', 'image_2.png', 'image_3.png'])

Perform OCR on multiple images using multiple workers (multiprocessing):

app.perform_batch_ocr(['image_1.png', 'image_3.png', 'image_2.png'], workers=2)

Using Command Line Interface

Typical usage with several options:

google-ocr --client-secret client_secret.json \
--upload-folder-id <google-drive-folder-id>  \
--image-dir images/ --extension .jpg \
--workers 4 --no-keep

Show help message with the full set of options:

google-ocr --help

Configuration

The default location for configuration is ~/.gdo.cfg.
If configuration is written to this location with a set of options,
we don't have to specify those options again on the subsequent runs.

Save configuration and exit:

google-ocr --client-secret client_secret.json --write-config ~/.gdo.cfg

Read configuration from a custom location (if it was written to a custom location):

google-ocr --config ~/.my_config_file ..

Performing OCR

Note: It is assumed that the client-secret option is saved in configuration file.

Single image file:

google-ocr -i image.png

Multiple image files:

google-ocr -b image_1.png image_2.png image_3.png

All image files from a directory with a specific extension:

google-ocr --image-dir images/ --extension .png

Multiple workers (multiprocessing):

google-ocr -b image_1.png image_2.png image_3.png --workers 2

PDF files:

google-ocr --pdf document.pdf --pages 1-3 5 7-10 13

DEV Community: Hrishikesh Terdalkar

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