Mock sample for your project: TAGGUN Receipt OCR Scanning API
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TAGGUN Receipt OCR Scanning API
taggun.io
Version: 1.5.74
Speed up your application development by using "TAGGUN Receipt OCR Scanning API" ready-to-use mock sample. Mocking this API will allow you to start working in no time. No more accounts to create, API keys to provision, accesses to configure, unplanned downtime, just work.
It also improves your integration tests' quality and reliability by accounting for random failures, slow response time, etc.
Description
Expects only running software, real reactions, and beautifully crafted APIs to serve your every desire to transcribe a piece of paper to digital form.
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To move from credit card per-transaction to monthly invoicing, also contact us.
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Authentication
For all calls, supply your API key. Sign up to obtain the key .
Our API supports both unencrypted (HTTP) and encrypted (HTTPS) protocols.
However, for security reasons, we strongly encourage using only the encrypted version.
The API key should be supplied as either a request parameter user_key or in Authorization header.
Authorization: user_key
API operations
All API operations can perform analysis on supplied raw text or on text extracted from a given URL.
Optionally, one can supply additional information which can make the result more precise. An example
of such information would be the language of text or a particular text extractor for URL resources.
The supported types of analyses are:
lemmatization ⟶
Finds out lemmata (basic forms) of all the words in the document.
correction ⟶
Performs correction (diacritization) on all the words in the document.
topic detection ⟶
Determines a topic of the document, e.g. finance or sports.
sentiment analysis ⟶
Determines a sentiment of the document, i.e. how positive or negative the document is.
named entity recognition ⟶
Finds named entities (like person, location, date etc.) mentioned the the document.
Encoding
The supplied text is expected to be in UTF-8 encoding, this is especially important for non-english texts.
Returned values
The API calls always return objects in serialized JSON format in UTF-8 encoding.
If any error occurs, the HTTP response code will be in the range 4xx (client-side error) or
5xx (server-side error). In this situation, the body of the response will contain information
about the error in JSON format, with exception and message values.
URL limitations
All the requests are semantically GET. However, for longer texts, you may run into issues
with URL length limit. Therefore, it's possible to always issue a POST request with all
the parameters encoded as a JSON in the request body.
Example:
POST /s1/sentiment
Content-Type: application/json
{"text":"There is no harm in being sometimes wrong - especially if one is promptly found out."}
This is equivalent to GET /s1/sentiment?text=There%20is%20no%20harm...
Request limitations
The API has other limitations concerning the size of the HTTP requests. The maximum allowed size of any
POST request body is 512 KiB. For request with a URL resource, the maximum allowed number of
extracted characters from each such resource is 100,000.
Terms of Service
By using the API, you agree to our
Terms of Service Agreement.
More information
The Interpretor Public Documentation
For all calls, supply your API key. Sign up to obtain the key .
Our API supports both unencrypted (HTTP) and encrypted (HTTPS) protocols.
However, for security reasons, we strongly encourage using only the encrypted version.
The API key should be supplied as either a request parameter user_key or in Authorization header.
Authorization: user_key
API operations
All API operations can perform analysis on supplied raw text or on text extracted from a given URL.
Optionally, one can supply additional information which can make the result more precise. An example
of such information would be the language of text or a particular text extractor for URL resources.
The supported types of analyses are:
lemmatization ⟶
Finds out lemmata (basic forms) of all the words in the document.
correction ⟶
Performs correction (diacritization) on all the words in the document.
topic detection ⟶
Determines a topic of the document, e.g. finance or sports.
sentiment analysis ⟶
Determines a sentiment of the document, i.e. how positive or negative the document is.
named entity recognition ⟶
Finds named entities (like person, location, date etc.) mentioned the the document.
Encoding
The supplied text is expected to be in UTF-8 encoding, this is especially important for non-english texts.
Returned values
The API calls always return objects in serialized JSON format in UTF-8 encoding.
If any error occurs, the HTTP response code will be in the range 4xx (client-side error) or
5xx (server-side error). In this situation, the body of the response will contain information
about the error in JSON format, with exception and message values.
URL limitations
All the requests are semantically GET. However, for longer texts, you may run into issues
with URL length limit. Therefore, it's possible to always issue a POST request with all
the parameters encoded as a JSON in the request body.
Example:
POST /s1/sentiment
Content-Type: application/json
{"text":"There is no harm in being sometimes wrong - especially if one is promptly found out."}
This is equivalent to GET /s1/sentiment?text=There%20is%20no%20harm...
Request limitations
The API has other limitations concerning the size of the HTTP requests. The maximum allowed size of any
POST request body is 512 KiB. For request with a URL resource, the maximum allowed number of
extracted characters from each such resource is 100,000.
Terms of Service
By using the API, you agree to our
Terms of Service Agreement.
More information
The Interpretor Public Documentation
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