Advanced OCR Language Translation_GPT-4o
설명
This combination of apps utilizes advanced OCR technology to extract text from various file formats. It then divides the text into smaller segments for tasks like translation or document review. The algorithm follows a step-by-step process, including context analysis, preparatory processing, translation, post-processing, and quality check. The output includes the translated texts and relevant analysis and notes.
워크플로우
Input
VAR_1-INPUT 0
1. Text Extractor for Images and Documents
모델: Text Extraction
프롬프트:
This app leverages advanced OCR technology to extract text from a wide range of file formats including PDF, DOCX, PPTX, XLSX, TXT, JPEG, PNG, JPG, and HTML. Quickly and accurately convert your images and documents into text.
Output:
1: Text Extractor for Images and Documents2. Split TextLogic
Input:
1: Text Extractor for Images and DocumentsSplit Method:
token
Chunk Size:
4000 tokens
Output:
2: Split Text3. Loop / ReduceLoop
Input:
2: Split TextInternal Module:
Korean Text Translation Analysis Framework
Model: gpt-4o
Analyze and translate Loop Item provided in the input variable. Follow this pseudo-code algorithm:
```pseudo
function analyze_and_translate(input):
// Step 1: Context Analysis
context = extract_context(input)
text_type = identify_text_type(input)
audience = identify_target_audience(input)
tone = determine_tone(input)
cultural_elements = identify_cultural_elements(input)
// Step 2: Preparatory Processing
preprocessed_text = preprocess(input)
terms = extract_key_terms(preprocessed_text)
idioms = identify_idioms(preprocessed_text)
// Step 3: Translation
translation = initialize_translation()
for sentence in preprocessed_text:
translated_sentence = translate_sentence(sentence, context, tone)
translation.append(translated_sentence)
// Step 4: Post-processing
refined_translation = refine_translation(translation, text_type, audience)
cultural_adaptations = adapt_cultural_elements(refined_translation, cultural_elements)
final_translation = apply_adaptations(refined_translation, cultural_adaptations)
// Step 5: Quality Check
if quality_check(final_translation, input):
return final_translation
else:
return revise_translation(final_translation, input)
// Main Execution
korean_text = input
english_translation = analyze_and_translate(korean_text)
// Output
print "Context Analysis:"
print context, text_type, audience, tone, cultural_elements
print "English Translation:"
print english_translation
```
Provide your translation and explanatory notes based on the pseudo-code algorithm above.
```pseudo
function analyze_and_translate(input):
// Step 1: Context Analysis
context = extract_context(input)
text_type = identify_text_type(input)
audience = identify_target_audience(input)
tone = determine_tone(input)
cultural_elements = identify_cultural_elements(input)
// Step 2: Preparatory Processing
preprocessed_text = preprocess(input)
terms = extract_key_terms(preprocessed_text)
idioms = identify_idioms(preprocessed_text)
// Step 3: Translation
translation = initialize_translation()
for sentence in preprocessed_text:
translated_sentence = translate_sentence(sentence, context, tone)
translation.append(translated_sentence)
// Step 4: Post-processing
refined_translation = refine_translation(translation, text_type, audience)
cultural_adaptations = adapt_cultural_elements(refined_translation, cultural_elements)
final_translation = apply_adaptations(refined_translation, cultural_adaptations)
// Step 5: Quality Check
if quality_check(final_translation, input):
return final_translation
else:
return revise_translation(final_translation, input)
// Main Execution
korean_text = input
english_translation = analyze_and_translate(korean_text)
// Output
print "Context Analysis:"
print context, text_type, audience, tone, cultural_elements
print "English Translation:"
print english_translation
```
Provide your translation and explanatory notes based on the pseudo-code algorithm above.
Output:
3: Loop / ReduceAdvanced OCR Language Translation_GPT-4o
최종 결과물 템플릿
module_3602429674794_individual
정보
만든 사람: Sung Lee
11
실행 수
1
즐겨찾기
89260.00s
평균 실행 시간
$0.30
평균 실행 비용
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